Computer program, game system used for the same, and control method

Abstract

To provide a computer program capable of assisting estimation of a concealed element in an imperfect information game.SOLUTION: A computer program PG2 causes a control unit 31 incorporated into a user device 3 to function as the undermentioned means in an imperfect information game played by a plurality of users using a deck card (a bunch of card objects CO) of each user. Namely, the computer program PG2 causes the control unit 31 to function as: means for acquiring estimation information using an estimation model part 24 (learned artificial intelligence model) set so as to acquire estimation information for estimating contents of at least one of deck cards of another user not shared by one user in the imperfect information game; and means for providing the estimation information.SELECTED DRAWING: Figure 8

Description

Technical Field

[0001] The present invention relates to a computer program or the like applied to a computer incorporated in a game system that provides element information as information regarding at least one of element groups for each user in a game played by a plurality of users using element groups for each user.

Background Art

[0002] There is a game system that provides element information as information regarding at least one of element groups for each user in a game played by a plurality of users using element groups for each user. For example, as a game, an element group, and element information, a system is known in which shogi in the real space, a move in the shogi, and information such as "winning rate" for each move are respectively adopted (see, for example, Non-Patent Document 1).

Prior Art Documents

Non-Patent Documents

[0003]

Non-Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In Non-Patent Document 1, a technique is disclosed in which information such as "winning rate", "candidate moves", and "lines of play" simulated by AI (artificial intelligence) for each move is added to a relay screen obtained by photographing a shogi board during a game from above. Games such as shogi are a type of game called a so-called perfect information game in which all information about the pieces used by each other is disclosed. On the other hand, there are also types of games called imperfect information games. In that type of game, at least one element of a group of elements (for example, options) used by other users (including computers) progresses without being shared with one user. For this reason, in an imperfect information game, information regarding elements that are not shared (hidden) with users may be important.

[0005] Therefore, an object of the present invention is to provide a computer program or the like that can assist in inferring elements hidden in an imperfect information game.

Means for Solving the Problems

[0006] The computer program of the present invention is a computer incorporated into a game system that provides element information as information regarding at least one of the element groups for each user in a game played by a plurality of users using the element groups for each user. In an imperfect information game of a type in which at least one content of an element group of another user provided as the game is not shared with one user, information acquisition means for acquiring the inference information using a predetermined process set so as to obtain inference information for inferring the content of the at least one element, and information providing means for providing the inference information as the element information. It is configured to function as

[0007] On the one hand, the game system of the present invention is a game system incorporated with a computer that provides element information as information regarding at least one of the element groups for each user in a game played by a plurality of users using the element groups for each user. In an incomplete information game of a type where at least one content of the element group of another user provided as the game is not shared with one user, the computer functions as an information acquisition means for acquiring the speculation information using a predetermined process set so as to obtain speculation information for speculating the content of the at least one element, and an information provision means for providing the speculation information as the element information.

[0008] Further, the control method of the present invention is for a computer incorporated in a game system that provides element information as information regarding at least one of the element groups for each user in a game played by a plurality of users using the element groups for each user. In an incomplete information game of a type where at least one content of the element group of another user provided as the game is not shared with one user, the computer is caused to execute a procedure for acquiring the speculation information using a predetermined process set so as to obtain speculation information for speculating the content of the at least one element, and a procedure for providing the speculation information as the element information.

Brief Description of Drawings

[0009]

Figure 1

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Embodiments for Carrying Out the Invention

[0010] (Overall Configuration) Hereinafter, with reference to the drawings, a control method according to an embodiment of the present invention, a game system in which a computer program is implemented (a game system according to an embodiment of the present invention), etc. will be described. First, with reference to FIG. 1, the overall configuration of a network system to which a game system according to an embodiment of the present invention is applied will be described. As shown in FIG. 1, the network system 1 is configured as a client-server type system including a plurality of user devices 3 as clients and a game server 2 connected to each user device 3 via a network NT.

[0011] The user device 3 is a device for daily use by the user, and is a computer device (information communication terminal device) having an information communication function via the network NT. The user device 3 implements a computer program according to an aspect of the present invention and functions as a game system according to an aspect of the present invention in the network system 1. As an example, a smartphone having a communication call function or a tablet terminal may be used as the user device 3. The user device 3 may be a PC (abbreviation for personal computer), or may be a personal or household game machine provided as a so-called consumer game machine. Further, as the user device 3, a business-use game machine provided as a so-called arcade game machine may be used.

[0012] The user device 3 functions as a game machine by implementing a predetermined software (application) and provides a game. The user device 3 provides a game including a selection opportunity for the user to select a part of a plurality of options. Such a game may be configured as an appropriate game such as a role-playing game, a simulation game, or an action game. As an example, it is configured as a battle-type video game in which the user of the user device 3 and an opponent (another user including a computer) battle according to a predetermined rule via a game screen including a plurality of objects. In a battle-type game, the user and the opponent may function as a member of a team, and the battle-type game may be played in a one-to-many (including the case where a plurality of users other than oneself are independent opponents as in mahjong) or many-to-many format. Hereinafter, an example of the case where it is played in a one-to-one format will be described.

[0013] In addition, the competitive game may be provided as a perfect information game such as shogi or chess (both are games of the type that use pieces as objects), but as an example, it is provided as an imperfect information game. An imperfect information game is a type of game in which, for a perfect information game where all the contents of various elements necessary for selection in a selection opportunity are shared with the user, at least one element of the group of elements (e.g., the contents of cards in a card game) used by other users (including computers) is not shared with the user. Various games such as mahjong are included in imperfect information games, but below, the case where a card game is provided as an example thereof will be described.

[0014] The game server 2 may be configured by appropriately combining a plurality of server units (server devices), or may be configured by a single server unit. The game server 2 may be configured as a cloud server using cloud computing technology. The game server 2 provides various services related to the game to the user device 3. This service includes an analysis service and an estimation service. Both the analysis service and the estimation service are services for assisting selection in the selection opportunity of the game. Specifically, the analysis service is configured to provide the user with at least one option before selection with the superiority information regarding the superiority or inferiority of the influence given by each option included in the selection opportunity as an analysis result. On the other hand, the estimation service is configured to provide estimation information regarding information not shared with the user in the card game. The details of the analysis service and the estimation service will be described later.

[0015] Note that the game server 2 may also provide the user device 3 with, for example, a distribution service for distributing computer programs and various data necessary for the user device 3 to play the game, a matching service for matching users who cooperate or compete in the game, a relay service for relaying game information to be shared between the user devices 3, and the like.

[0016] The network NT may be configured as appropriate as long as it can connect the user device 3 to the game server 2. As an example, the network NT is configured to realize network communication using the TCP / IP protocol. Typically, the network NT is configured by combining the Internet as a WAN and an intranet as a LAN. In the example of FIG. 1, the game server 2 is connected to the network NT via the router NTr, and the user device 3 is connected to the network NT via the access point PP, respectively.

[0017] (Control system of the network system) Next, the main part of the control system of the network system 1 will be described with reference to FIG. 2. First, the game server 2 is provided with a control unit 21 and a storage unit 22 as a storage means. The control unit 21 is configured as a computer that combines a processor unit that executes various arithmetic processes and operation controls according to a predetermined computer program, and internal memory and other peripheral devices necessary for its operation. The processor unit may appropriately include units such as a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), and an NPU (Neural network Processing Unit) (including cases where each processor unit is integrated as appropriate, such as when the GPU is incorporated into the CPU).

[0018] The storage unit 22 is an external storage device realized by a storage unit including a non-volatile storage medium such as a hard disk array (a computer-readable storage medium). The storage unit 22 may be configured to hold all data on one storage unit, or may be configured to store data distributed across a plurality of storage units. The storage unit 22 records the server program PG1 and the server data SD. The server program PG1 is a computer program that causes the control unit 21 to execute processes necessary to provide various services to the user device 3. The server program PG1 may appropriately include various programs according to the processes to be executed by the control unit 21, and in the example of FIG. 2, includes an analysis program AP and a speculation program FP.

[0019] The analysis program AP is a computer program for causing the control unit 21 to execute various processes for realizing an analysis service. The analysis program AP may be appropriately configured. For example, it may be configured to cause the control unit 21 to execute a process of outputting an analysis result based on a predetermined logic (including predetermined rules such as rules). However, in the example of FIG. 2, it is configured to cause the control unit 21 (for example, a GPU) to function as an artificial intelligence model that outputs an analysis result. The analysis program AP may be appropriately configured, but in the example of FIG. 2, it is configured as an inference program incorporating the analysis learned data (parameters) AD output by causing the learning data set (for analysis) to be learned by the learning program (for analysis).

[0020] The learned data AD for analysis may be of one type, but in the example of FIG. 2, it includes multiple types of learned data AD. The multiple types of learned data AD for analysis may be generated by applying the same learning data set to learning programs with different algorithms (learning methods), but as an example, they are generated by applying different learning data sets to the same learning program. The analysis program AP functions the control unit 21 as an artificial intelligence model with different thoughts by incorporating and executing each of the multiple types of learned data AD for analysis. The analysis program AP incorporating each of the multiple types of learned data AD for analysis is distinguished as the first analysis program AP1, the second analysis program AP2, etc. in the example of FIG. 2. For this reason, the analysis program AP is provided with a plurality of analysis programs AP such as these first analysis program AP1 and second analysis program AP2.

[0021] The inference program FP is a computer program for causing the control unit 21 to execute various processes for realizing an inference service. The inference program FP may be configured appropriately. For example, it may be configured to cause the control unit 21 to execute a process of outputting an inference result based on a predetermined logic. However, in the example of FIG. 2, it is configured to function the control unit 21 (e.g., GPU) as an artificial intelligence model in the same manner as the analysis program AP. The inference program FP may also be configured appropriately, but in the example of FIG. 2, it is configured as an inference program incorporating the learned data (parameters) FD for inference output by causing a learning data set (for inference) to be learned by a learning program (for inference).

[0022] The learned data FD for speculation may be of one type, but in the example of FIG. 2, like the learned data AD for analysis, it includes multiple types of learned data FD for speculation (which does not have to match the number of the learned data AD for analysis). The multiple types of learned data FD for speculation may be generated by applying the same learning data set to learning programs with different algorithms (learning methods). As an example, however, they are generated by applying different learning data sets to the same learning program. The speculation program FP causes the control unit 21 to function as an artificial intelligence model with different ways of thinking by incorporating and executing each of the multiple types of learned data FD for speculation. The speculation programs FP incorporating each of the multiple types of learned data FD for speculation are distinguished as the first speculation program FP1, the second speculation program FP2, etc. in the example of FIG. 2. For this reason, the speculation program FP is provided with multiple speculation programs FP such as these first speculation program FP1 and second speculation program FP2.

[0023] The server data SD is data referred to by the server program PG1 to provide various services. The server data can include appropriate data related to various services, but in the example of FIG. 2, as an example, play data PD, learned data AD for analysis, and learned data FD for speculation are shown. The play data PD is data in which information regarding the past play performance of each user is described. The play data PD may include not only play performance but also other information necessary for managing each user, such as various personal information including the gender or address of each user. The possessions of each user within the card game can be managed as appropriate, but as an example, they are managed by the play data PD. Specifically, the card game is configured to grant card objects to users who satisfy predetermined granting conditions (such as purchase, lending, lottery, or granting due to game progress). Then, the card objects granted to each user (hereinafter sometimes referred to as owned cards) are managed so as to be associated with each user in the play data PD.

[0024] The control unit 21 can be provided with various logical devices by combining the hardware resources of the control unit 21 and the server program PG1 as software resources. In the example of FIG. 2, an analysis model unit 23 and a speculation model unit 24 are provided. Both the analysis model unit 23 and the speculation model unit 24 are logical devices that function as artificial intelligence models. Specifically, the analysis model unit 23 functions as an artificial intelligence model that outputs an analysis result based on the analysis program AP, and the speculation model unit 24 functions as an artificial intelligence model that outputs a speculation result based on the speculation program FP.

[0025] The analysis model unit 23 (artificial intelligence model) is realized, for example, by a combination of the learned analysis data AD and the analysis program AP (the analysis program AP that incorporates the learned analysis data AD) and executes various processes for realizing the analysis service. This type of artificial intelligence model can form different thoughts depending on various elements. For example, it tends to have different thoughts due to at least one difference among the content of the learning data set, the algorithm of the learning program, and the algorithm of the inference program. The analysis model unit 23 can be provided with an appropriate type of analysis model unit 23 according to these differences. In the example of FIG. 2, two types of analysis model units 23, i.e., a first analysis model unit 23A and a second analysis model unit 23B, are shown.

[0026] The first analysis model unit 23A is an analysis model unit 23 corresponding to the first analysis program AP1. Similarly, the second analysis model unit 23B is an analysis model unit 23 corresponding to the second analysis program AP2. The first analysis model unit 23A and the second analysis model unit 23B execute various processes according to the first analysis program AP1 or the second analysis program AP2. For example, the process includes alternative analysis processing. The details of the procedure of the alternative analysis processing will be described later.

[0027] Similarly, the estimation model unit 24 (artificial intelligence model) is realized, for example, by a combination of the learned data FD for estimation and the estimation program FP (the estimation program FP that incorporates the learned data FD for estimation) and executes various processes for realizing the estimation service. An appropriate type (including one type) of the estimation model unit 24 may be provided in the estimation model unit 24. In the example of FIG. 2, two types of the estimation model units 24, i.e., the first estimation model unit 24A and the second estimation model unit 24B, are shown.

[0028] The first estimation model unit 24A is an estimation model unit 24 corresponding to the first estimation program FP1. Similarly, the second estimation model unit 24B is an estimation model unit 24 corresponding to the second estimation program FP2. The first estimation model unit 24A and the second estimation model unit 24B execute various processes according to the first estimation program FP1 or the second estimation program FP2. For example, the process includes a backside estimation process. Details of the procedure of the backside estimation process will be described later.

[0029] Note that, in the control unit 21, for example, a Web service management unit or the like for realizing processes related to various services such as the above-described distribution service, matching service, or relay service may be provided as a logical device. Similarly, an input device such as a keyboard and an output device such as a monitor may be connected to the control unit 21 as necessary. However, their illustrations are omitted.

[0030] On the other hand, the user device 3 is provided with a control unit 31 and a storage unit 32 as a storage means. The control unit 31 is configured as a computer that combines a processor unit that executes various arithmetic processes and operation controls according to a predetermined computer program and an internal memory and other peripheral devices necessary for its operation. The processor unit may appropriately include units such as a CPU, a GPU, and an NPU (including cases where each processor unit is integrated as appropriate, such as when a GPU is incorporated into a CPU), similar to the game server 2.

[0031] The memory unit 32 is an external storage device realized by a storage unit including a non-volatile storage medium (a computer-readable storage medium) such as a hard disk or a semiconductor storage device. The game program PG2 and the game data GD are stored in the memory unit 32. The game program PG2 is a computer program that causes the control unit 21 to execute processes necessary for the user device 3 to function as a game device. The game data GD is data referred to by the game program PG2 for providing the game. An analysis program AP or an inference program FP may be appropriately provided to the user device 3 from the game server 2. In that case, the game data GD may include the analysis program AP or the inference program FP.

[0032] The game data GD may appropriately include various types of data (including various tables) necessary for playing the game, such as image data for displaying various images for the game, or audio data for playing various sounds (including BGM such as music), or card data defining each card object. In the example of FIG. 2, play data PD is shown. The play data PD is provided from the game server 2 and stored as needed. Also, when the analysis program AP or the inference program FP is provided to the user device 3 from the game server 2, the game data GD may include the analysis learned data AD and the inference learned data FD.

[0033] In the control unit 31, a progress control unit 33, a data management unit 34, and a model processing unit 35 are provided as logical devices realized by a combination of the hardware resources of the control unit 31 and the game program PG2 as software resources.

[0034] The progress control unit 33 executes various processes necessary for the progress of the card game. The processes include those necessary to enjoy the services provided by the game server 2. For example, the progress control unit 33 executes processes such as collaborating with the web service management unit to perform processes related to matching with an opponent, reflecting the play of the opponent during the game in its own progress, or conversely, reflecting its own play in the opponent's progress. Further, the progress control unit 33 also executes processes such as providing selection opportunities and switching between each turn and phase, which will be described later.

[0035] The data management unit 34 executes various processes related to the management of the game data GD. For example, when a card object is given to a user, the data management unit 34 executes a process to reflect the card object as an owned card in the play data PD.

[0036] The model processing unit 35 executes various processes necessary to provide an analysis service and an inference service to the user of the user device 3. The processes executed by the model processing unit 35 include processes realized in cooperation with the analysis model unit 23 or the inference model unit 24 of the game server 2. For example, the model processing unit 35 executes a process to analyze the influence that at least one of a plurality of selectable options in a selection opportunity has on the progress of the card game in cooperation with the analysis model unit 23 of the game server 2 and provide the result as an analysis result. Similarly, the model processing unit 35 executes a process to provide inference information regarding information not shared with the user in the card game in cooperation with the inference model unit 24 of the game server 2.

[0037] For example, as a process to realize the analysis service, the model processing unit 35 executes an option analysis process in cooperation with the analysis model unit 23 of the game server 2. The model processing unit 35 also executes a display change process. Similarly, as a process to realize the inference service, the model processing unit 35 executes a reverse side inference process in cooperation with the inference model unit 24 of the game server 2. The details of the procedure of the display change process will be described later.

[0038] Further, the model processing unit 35 generates a situation log SL necessary for the analysis by the analysis model unit 23 or the speculation by the speculation model unit 24, causes the internal storage device of the control unit 31 to hold it, and also executes a process of appropriately updating the situation log SL as the situation changes. The situation log SL may appropriately include various types of information regarding the situation of the game. The situation log SL (data) includes, for example, information on all card objects currently in use, information on each card object arranged in various arrangement locations described later, attributes of each card object (including various states of each card object such as summoning described later or leaving the field area), life points, phases, and information on selectable options in each situation, that is, various types of information (which may be limited to an appropriate part of the information) for determining the situation of the card game.

[0039] The user device 3 is provided with appropriate output devices and input devices. In the example of FIG. 2, a monitor MO, a speaker SK, and a touch sensor TS are shown as examples of the output devices and the like. All of them are general-purpose hardware provided in an information communication terminal such as a smartphone. For example, the touch sensor TS is an input device that inputs a signal corresponding to a touch operation (an operation of touching with a finger) of the user to the control unit 31. The speaker SK is an output device for reproducing various voices according to a signal from the control unit 31. The monitor MO is an output device (display device) for presenting a game screen or the like according to a signal from the control unit 31. Note that the user device 3 may be appropriately provided with various devices such as a gyro sensor, an acceleration sensor, and a position information (for example, GPS information) receiving device.

[0040] (Overview of the game) Next, the overview of the card game will be described with reference to FIGS. 3 to 11. The card game may be configured as an appropriate game, but as an example, it is configured as a video game played through a game screen including a plurality of card objects. More specifically, the card game is configured to be played using a deck of card objects (a group of a predetermined number of card objects) selected by the user for play from the owned card group.

[0041] FIG. 3 schematically shows an example of a battle screen for playing a card game. The battle screen 50 is a game screen displayed when a user and an opponent play against each other using their respective deck cards. Various card objects can be arranged (displayed) on the battle screen 50. In the example of FIG. 3, for convenience of explanation, the card object CO facing the front side (the side where the content of the card object can be visually recognized), regardless of its type and content, is shown by dots, and the card object facing the back side (the side where the content of the card object cannot be visually recognized and is hidden) is shown by diagonal lines. In this example, a predetermined number (which may be fixed, variable, or not equal to the number of the opponent's deck cards) of card objects CO included in the user's deck cards function as a plurality of objects and card objects of the present invention. Also, the owned card group and the deck card respectively function as the denominator element group and two or more elements of the present invention.

[0042] As shown in FIG. 3, the battle screen 50 includes a user area 51, a shared area 52, and an opponent area 53. The user area 51 is an area dedicated to the user. The user area 51 can be configured as appropriate. In the example of FIG. 3, it includes a field area 54, a main deck placement area 55, an extra deck placement area 56, a hand placement area 57, a graveyard area 58, and a point display area LR.

[0043] The field area 54 is an area where a plurality of card placement areas CP (only some are labeled) are formed. Each card placement area CP is a place (area) where each card object CO for advancing the game among the deck cards should be placed. In a card game, appropriate types of card objects CO with various roles (uses), effects, etc. can be prepared. For example, a card object CO representing a monster (hereinafter sometimes referred to as "monster card CO1"), a card object CO representing magic (a predetermined effect) (hereinafter sometimes referred to as "magic card CO2"), a card object CO representing a trap (an effect different from magic) (hereinafter sometimes referred to as "trap card CO3"), or a card object CO representing a special effect (hereinafter sometimes referred to as "special card CO4") and the like are prepared. When a card object CO is placed in each card placement area CP, that card object CO actually exerts its role and the like in the battle.

[0044] For example, the monster card CO1 is given roles such as an opponent in a battle, an attack on the opponent's monster card CO1, or a defense. Also, the monster card CO1 can be classified into various types. As an example, it includes two types: a normal monster card CO1 and an extra monster card CO1. The normal monster card CO1 is a monster card CO1 that can be placed from the hand placement area 57 to the field area 54. On the other hand, the extra monster card CO1 is a monster card CO1 that is summoned (called) in exchange for the monster card CO1 leaving the field area 54. The placement (summoning) of the extra monster card CO1 is usually restricted to the shared area 52, but may be allowed to be placed in the field area 54 when certain field conditions are met. For this reason, the monster card CO1 may have the role of summoning (calling) other monster cards CO1 to the field area 54, etc. And the monster card CO1 placed in the card placement area CP is used to actually give roles such as attack, defense, or summoning to the progress of the game. Similarly, the magic card CO2, the trap card CO3, and the special card CO4 are card objects CO that can be placed from the hand placement area 57 to the field area 54, and the magic card CO2, the trap card CO3, and the special card CO4 placed in the card placement area CP are used for the activation of the effects indicated by each card object CO.

[0045] Multiple card placement areas CP may be appropriately placed in the field area 54. In the example of FIG. 3, they are arranged to form two rows, a front row 54A located closer to the opponent's area 53 and a rear row 54B located behind it. The front row 54A and the rear row 54B are rows formed by six card placement areas CP and five card placement areas CP arranged side by side, respectively. Any card object CO can be placed in each card placement area CP without restriction, but as an example, there are restrictions on the types of card objects CO that can be placed. The front row 54A and the rear row 54B may be appropriately used separately, but as an example, they are used separately according to the types of card objects CO that can be placed.

[0046] Specifically, the front row 54A is used as each card placement area CP where the monster card CO1 among the types of card objects CO should be placed. However, only the leftmost card placement area CP among the six card placement areas CP forming the front row 54A (which can be formed as appropriate, but in the example of FIG. 3, it is formed slightly smaller than the other card placement areas CP) is an exception and is used as each card placement area CP where the special card should be placed. That is, the placement of card objects CO such as the magic card CO2 or the trap card CO3 on the front row 54A is restricted, the placement of the special card CO4 on the front row 54A except for the leftmost card placement area CP is restricted, and the placement of the monster card CO1 is further restricted at the leftmost card placement area CP.

[0047] On the other hand, the rear row 54B is used as each card placement area CP where the magic card CO2 and the trap card CO3 among the types of card objects CO should be placed. That is, the placement of card objects CO such as the monster card CO1 or the special card CO4 on the rear row 54B is restricted. Note that a monster card CO1 having the characteristics of the magic card CO2 (the monster card CO1 corresponding to a monster having a magic effect) may be prepared, and the placement of such a monster card CO1 having a magic attribute on a predetermined card placement area CP (for example, the card placement areas CP at both the left and right ends) of the rear row 54B may be permitted. That is, card objects CO having appropriate multiple attributes may be included, and the placement of such card objects CO may be permitted in both the front row 54A and the rear row 54B according to the multiple attributes.

[0048] In the field area 54, the card object CO can be appropriately arranged according to the user's instruction under the limitation for each card placement area CP. In the example of FIG. 3, one monster card CO1 is arranged in the third card placement area CP from the right in the front row 54A. Also, each card object CO may be arranged to face various directions such as vertically or horizontally in the card placement area CP, and the arrangement directions such as vertical can be appropriately used separately. As an example, when the monster card CO1 is used for defense, it is arranged horizontally, and when it is used for attack, it is arranged vertically, respectively.

[0049] The hand arrangement area 57 is an area where the card object CO (hereinafter sometimes referred to as hand CO5) that is virtually in hand among the deck cards should be arranged (displayed). The hand CO5 is a candidate card object CO to be arranged in the field area 54 (each card placement area CP). An appropriate number of hand CO5 can be arranged in the hand arrangement area 57. In the example of FIG. 3, five hand CO5 are arranged. In this example, the hand arrangement area 57 functions as the hand area of the present invention.

[0050] The main deck placement area 55 and the extra deck placement area 56 are both areas where card objects CO indicating the remaining deck cards (the remainder after excluding the card objects CO in the field area 54 and the hand placement area 57 from the initial deck cards) should be placed, but their uses are different. Specifically, the main deck placement area 55 is an area where card objects CO that are candidates to be added to the hand CO5 among the remaining deck cards (hereinafter sometimes referred to as the deck CO6 in the deck) should be placed. The card objects CO of the deck CO6 are added to the hand CO5 through drawing in the draw phase described later. On the other hand, the extra deck placement area 56 is an area where a group of card objects (hereinafter sometimes referred to as the summoned deck card CO7) as a bundle of extra monster card CO1 should be placed. When the summoning of the extra monster card CO1 is executed via a normal monster card CO1, the extra monster card CO1 to be placed in the field area 54 is drawn from the summoned deck card CO7. That is, the remaining deck cards are divided into the deck CO6 and the summoned deck card CO7 and placed in the main deck placement area 55 and the extra deck placement area 56 respectively. In this example, the main deck placement area 55 functions as the deck area of the present invention.

[0051] The graveyard area 58 is, in principle, an area for accommodating card objects CO that have left the field area 54. For example, the monster card CO1 leaves the field area 54 when it meets certain conditions such as being attacked by the opponent's monster card CO1 or being summoned by the extra monster card CO1 (conditions for leaving the field area 54 etc.). Similarly, card objects CO that exert certain effects, such as the magic card CO2 and the trap card CO3, also leave the field area 54 according to the card effect after their effects are activated. The graveyard area 58 is provided as a place (destination of movement) for accommodating (placing) the card objects CO that have left the field area 54. The graveyard area 58 may be configured to display the card objects CO that have left the field area 54, but in the example of FIG. 3, the display of the card objects CO is configured to be omitted.

[0052] The point display area LR is an area for displaying the user's life points. In a card game, appropriate life points can be set mutually for the user and the opponent, but in the example of FIG. 3, all life points are set to 8000 points ("8000"). The win / loss conditions for determining the win or loss in the battle can be set as appropriate. For example, it is satisfied when the opponent's life points reach zero. That is, the life points function as a parameter for determining the win or loss. Specifically, the user can reduce the opponent's life points by using the monster card CO1 placed in the field area 54 and the shared area 52 for an attack. If the opponent's life points can be reduced to zero, the user is determined to have won and the battle ends. On the other hand, if the user's life points reach zero due to an attack from the opponent's monster card CO1, the user is determined to have lost and the battle ends.

[0053] The shared area 52 is an area shared by the user and the opponent. The shared area 52 can be configured as appropriate. In the example of FIG. 3, two card placement areas CP are provided. In each card placement area CP, the user's card object CO and the opponent's card object CO are appropriately arranged. The placement of each card object CO in the shared area 52 to each card placement area CP may be unrestrictedly permitted regardless of the type of the card object CO, but is restricted to the extra monster card CO1 as an example. That is, the extra monster card CO1 summoned from the summoning deck card CO7 is first arranged in each card placement area CP of the shared area 52, and the placement in the field area 54 is permitted only when a predetermined field condition is satisfied. In this example, each card placement area CP (the extra deck placement area 56 may be included) of the user area 51 and the shared area 52 functions as a plurality of card placement areas of the present invention.

[0054] The opponent area 53 is an area dedicated to the opponent. The opponent area 53 is an area that plays the same role as the user area 51 for the opponent. For this reason, a field area 54, a main deck placement area 55, an extra deck placement area 56, a hand placement area 57, a graveyard area 58, and a point display area LR are also formed in the opponent area 53. However, since their roles and the like are the same as those of the user area 51, the description thereof is omitted.

[0055] FIG. 4 is a diagram showing an example of the progress procedure of the card game in the example of FIG. 3. As shown in FIG. 4, the card game includes the user's turn and the turn of the opponent (hereinafter sometimes referred to as a player when not distinguishing between the two). Then, through these turns, the game proceeds in a so-called turn-based manner where the turns are alternately repeated between the user and the opponent. Specifically, first, as game preparation, for example, the player's deck cards are shuffled and then placed in the main deck placement area 55, and a predetermined number of card objects CO are drawn (drawn) from each player's deck cards (the card pile CO6) and displayed (placed) in the hand placement area 57 as the hand CO5. Such processing is performed. When the preparation is completed, the game starts from the turn of the leading player (for example, the user is the leading player). One turn is divided into a plurality of phases. A phase is a concept for dividing the procedures to be performed in one turn into multiple stages according to their content and nature. In the example of FIG. 4, one turn is divided into six stages from the draw phase to the end phase, but this is only an example.

[0056] In each phase, the player whose turn it is can appropriately select actions within the range defined for each phase. An example is as follows. In the draw phase, a card object CO is drawn from the deck CO6. In the standby phase, the effects of the card objects CO designated as those for which effect processing should be performed in that phase can be activated. In the first main phase, various actions such as the summoning (calling) of various objects such as monsters to be used in battle, the setting of card objects CO with unique effects such as spells and traps, or the activation of effects are permitted while appropriately using the card objects CO in the field area 54. In the battle phase, battles (battles) using card objects CO are conducted. For example, a battle is conducted by selecting a monster card CO1 to be used in the user's turn's attack and a monster card CO1 to be the target of the opponent's attack (if there is no monster card CO1 in the field area 54, a direct attack on the life points). The result of the battle is determined according to parameters such as the attributes and strength of the monster card CO1. In the second main phase, the same actions as in the first main phase are permitted. In the end phase, the end of the turn is indicated (declared).

[0057] Note that the battle in the battle phase can be avoided by the choice of the player whose turn it is. In that case, the battle phase and the second main phase are skipped. Similarly, in the battle phase, the second main phase can also be avoided by the player's choice. The end of one phase is indicated by a predetermined end operation. When one turn ends, the turn passes to the opponent player. The game ends when a predetermined win / loss condition is met while the turns are repeated alternately. As an example, the win / loss condition is met when the life points set for each player P are reduced to a predetermined value (for example, 0) by battle.

[0058] (Analysis Service) Next, with reference to FIG. 5, the details of the analysis service will be described. The analysis service is a service that provides superiority and inferiority information as an analysis result for at least one option in the selection opportunity as described above. The analysis service can be applied to an appropriate situation including the selection opportunity, for example, applied to the selection opportunity included in the battle screen 50. FIG. 5 schematically shows an example of the battle screen 50 when the analysis service is applied. In the example of FIG. 5, the same components as those of the battle screen 50 in FIG. 3 are denoted by the same reference numerals and their description is omitted.

[0059] The battle in the battle screen 50 is played by repeatedly playing turns including a plurality of phases between players as shown in FIG. 4. For this reason, a phase end instruction for ending each phase (for example, entering the next battle phase in the case of the first main phase), or a turn end instruction for ending each turn, etc. can function as options in the battle screen 50. Also, in the battle screen 50, deck cards (card object groups) are used through each phase as described above and are placed in the field area 54 etc. via the hand CO5. For this reason, the user is required to make selections such as which hand CO5 to place in which card placement area CP and how. Selections such as at what timing and how to activate the effects for magic cards CO2 and trap cards CO3 are also required. Also, in the case of monster cards CO1, selections such as whether to attack in the battle phase and if so, which monster card CO1 to attack are also required. When the analysis service is applied to the battle screen 50, superiority and inferiority information regarding these options is provided.

[0060] Specifically, as shown in FIG. 5, when the analysis service is applied, an advice display column 60 is additionally displayed compared to the example of FIG. 3. The advice display column 60 is a column for displaying the superiority and inferiority information (analysis result) for each option provided by the analysis service. The presence or absence of the use of the analysis service, in other words, the presence or absence of the display of the advice display column 60 may be set as appropriate and may be displayed by a predetermined operation for requesting the display or the like, but as an example, it is automatically displayed for each selection opportunity without a request from the user.

[0061] An appropriate number of superiority and inferiority information may be displayed in the advice display column 60. For example, superiority and inferiority information regarding all options may be displayed. However, in the example of FIG. 5, five pieces of advice information are displayed via five advice sections 61, namely the first advice section 61A to the fifth advice section 61E. Also, in the analysis service, a plurality of artificial intelligence models provided in the game server 2 are used, and a plurality of superiority and inferiority information (analysis results) are output for each option via them. Therefore, a plurality of superiority and inferiority information output by a plurality of artificial intelligence models for each option is displayed in the advice display column 60. The number (type) of artificial intelligence models may be an appropriate number. However, in the example of FIG. 5, two types of artificial intelligence models, namely "attack-type AI" and "defense-type AI", are used, and two types of superiority and inferiority information are displayed for each option by them.

[0062] "Attack-type AI" and "defense-type AI" respectively indicate an attack-type artificial intelligence model (hereinafter sometimes referred to as attack-type AI) and a defense-type artificial intelligence model (hereinafter sometimes referred to as defense-type AI). And the five advice sections 61 in the advice display column 60 are classified into an advice section 61 that displays superiority and inferiority information based on the attack-type AI and an advice section 61 that displays superiority and inferiority information based on the defense-type AI. The advice section 61 corresponding to the attack-type AI is displayed in white in the example of FIG. 5. For example, the first advice section 61A, the third advice section 61C, and the fourth advice section 61D correspond to it. On the other hand, the advice section 61 corresponding to the defense-type AI is displayed in a gray color scheme in the example of FIG. 5. For example, the second advice section 61B and the fifth advice section 61E correspond to it.

[0063] Each artificial intelligence model is realized by the content of the learned data for analysis AD as an example as described above, and each learned data for analysis AD is generated by the content of the learning dataset. Then, the attack-type AI corresponds to a learned artificial intelligence model based on the learned data for analysis AD generated by an attack-type learning dataset, and the defense-type AI corresponds to a learned artificial intelligence model based on the learned data for analysis AD generated by a defense-type learning dataset. For example, among the plurality of types of analysis model units 23, the first analysis unit 23A corresponds to the attack-type AI, and the second analysis model unit 23B corresponds to the defense-type AI. The attack-type learning dataset and the defense-type learning dataset can be configured appropriately. For example, they are respectively composed of a dataset including a performance set that won with an attack-emphasized strategy and a dataset including a performance set that won with a defense-emphasized strategy.

[0064] Each advice unit 61 can be configured appropriately. In the example of FIG. 5, it includes an advice information unit 62 and an option information unit 63. The advice information unit 62 is a part that displays detailed information including superiority and inferiority information. The superiority and inferiority information may be appropriate information regarding the influence of each option, such as symbols indicating the degree of influence of each option on victory such as A, B, C, or character information that sensuously indicates the influence given by each option such as "likely to lead to victory". In the example of FIG. 5, it is configured as numerical information indicating the possibility of becoming advantageous in the progress of the battle through the magnitude of numerical values. For example, in the first advice unit 61A, numerical information "42.0%" is displayed as the superiority and inferiority information. The numerical information may be a numerical value indicating various advantages, such as a numerical value indicating the possibility of progressing advantageously (being difficult to become disadvantageous) within a predetermined range of turns or selections of the analysis target. In the example of FIG. 5, it is displayed as a probability value indicating the possibility (probability) of ultimately winning in the battle.

[0065] The probability value is information indicating the winning rate when compared with other options output by the same artificial intelligence model. That is, for example, if it is the probability value of an attack-type AI model, it is set so that the sum of the probability values of all options output by the attack-type AI model becomes 100%. The numerical information may be values independent of each other (the sum of the values of all options does not necessarily become 100%), such as the evaluation results for each option by an attack-type AI model, but as an example, it is calculated as a probability value that totals 100% in this way. In addition, the detailed information may include appropriate information in addition to the information of the probability value (superiority and inferiority information). In the example of FIG. 5, information indicating the artificial intelligence model that output the probability value, such as "Attack-type AI A type evaluation", is included. In the example of FIG. 5, only one type (for example, "A type") of both the attack-type AI and the defense-type AI is used. However, a plurality of artificial intelligence models of the same type (such as attack-type) based on different performance sets, such as "B type", may be used for outputting probability values.

[0066] The option information section 63 is a part that displays information on the options that are the target of the superiority and inferiority information (probability value). For example, in the option information section 63 of the first advice section 61A, information on the option of "Enter the battle phase" (an option corresponding to the start of the battle phase) is displayed. In this case, the first advice section 61A indicates that the probability value (superiority and inferiority information) of the option of "Enter the battle phase" is "42.0%". In the example of FIG. 5, the display of the advice information section 62 and the option information section 63 of the third advice section 61C to the fifth advice section 61E is simplified. However, for example, details may be displayed in the same way as the first advice section 61A by a touch operation on each advice section 61.

[0067] Also, in the example of FIG. 5, the probability value for the option of "entering the battle phase" is output only by the attack-type AI, but probability values for similar options are also output by the defense-type AI. Specifically, both the attack-type AI and the defense-type AI output probability values for all the options that can be selected in each situation of the battle screen 50. Therefore, at least two types of probability values corresponding to the attack-type AI and the defense-type AI respectively are output for each selectable option. The advice display column 60 may be configured to display all the information on the two types of probability values for each option, but in the example of FIG. 5, it is configured to display only the probability values of the top five options (some of the options).

[0068] The ranking of the probability values is evaluated for each artificial intelligence model such as the attack-type AI and the defense-type AI, and the top of each may be displayed in the advice display column 60, but in the example of FIG. 5, it is evaluated across the attack-type AI and the defense-type AI (different artificial intelligence models). For example, if "entering the battle phase" is evaluated as having the highest probability value in the attack-type AI, but as having a low probability value in the defense-type AI, only the analysis result of the attack-type AI may be displayed in the advice display column 60. Thus, as the probability value for the same option, only one of the attack-type AI and the defense-type AI may be displayed in the advice display column 60 in some cases, but as described above, both the attack-type AI and the defense-type AI output probability values for the same all options, and both of them can be displayed as advice in the advice display column 60.

[0069] (Calculation method of probability value) Next, with reference to FIGS. 6 and 7, a method for calculating the probability value (superiority / inferiority information) for each option output by each artificial intelligence model such as an attack-type AI will be described. FIG. 6 is an explanatory diagram for explaining a list of options (actions) that a player can take in a situation with a battle screen 50. In the example of FIG. 6, the options that a player can take (conceptualized by "○") are shown in a tree type that branches into the next option for each option. As shown in FIG. 6, the options that a player can take on the battle screen 50 are classified into layers of a plurality of option groups, and the option group in the deepest layer in each tree corresponds to each actually selectable option. Then, all the actually selectable options are input into a plurality of artificial intelligence models such as an attack-type AI and a defense-type AI, and a probability value for each option is output by each of the plurality of artificial intelligence models.

[0070] Specifically, in the example of FIG. 6, in a situation where three monster cards CO1 are already arranged in the front row 54A of the field area 54 in the example of FIG. 5, and three magic cards CO2 etc. are already arranged in the back row 54B, and two of "A monster card" and "B magic card" are held as hand cards CO5, the selectable option group is shown. In this case, the first options that the player can take are the use of the "A monster card", the use of the "B magic card", "phase transition", and "end of turn". Therefore, these four options form the first-layer option group that can be selected first.

[0071] In addition, for the use of "A Monster Card" and "B Magic Card", there are further options as to where and how to use them, and these form the second-layer option group. For example, in the field area 54 of the example in FIG. 5, the front row 54A is distinguished from the left end (the card placement area CP where the Monster Card CO1 cannot be placed) in order from the adjacent one to the right as the 1st card placement area CP to the 5th card placement area CP. When three Monster Cards CO1 are placed in the 3rd card placement area CP to the 5th card placement area CP, the card placement areas CP where the "A Monster Card" (Monster Card CO1) can be placed are the 1st card placement area CP and the 2nd card placement area CP. Therefore, these two card placement areas CP correspond to the options for the placement destinations of the "A Monster Card". Also, there are two types of placement methods for the Monster Card CO1 on the card placement area CP: "Summon" (the Monster Card CO1 is placed face-up) and "Set" (the Monster Card CO1 is placed face-down). Therefore, for each of the two options of the 1st card placement area CP and the 2nd card placement area CP, there are further two types of options: "Summon" and "Set". As a result, for the use of the "A Monster Card", there are four options corresponding to two card placement areas CP × two types of placement methods. Note that for the Monster Card CO1, there may be various other options depending on the situation. For example, in the case of the Extra Monster Card CO1, it is necessary to specify a predetermined number (for example, three) of Monster Cards CO1 that should be moved away from the field area 54 (should be moved to the Graveyard area 58) for its summoning. In this case, options such as the predetermined number (for example, three) × the type of the Extra Monster Card CO1 × its card placement area CP may be derived.

[0072] On one hand, for example, in the field area 54 of the example in FIG. 5, the rear row 54B is distinguished from the 6th card placement area CP to the 10th card placement area CP in order from the left end to the right. When three magic cards CO2 etc. are arranged in the 8th card placement area CP to the 10th card placement area CP, the card placement areas CP where the "B magic card" (magic card CO2) can be placed are the 6th card placement area CP and the 7th card placement area CP, two in number. Therefore, these two card placement areas CP correspond to the options for the placement destination of the "B magic card". Also, the "B magic card" can be configured as an appropriate magic card CO2. For example, if the "B magic card" is a magic card CO2 that has two placement methods, namely "activate" (option to activate the effect) and "set" (option to hold off on activating the effect) as the placement method to the card placement area CP, these two placement methods exist as options. As a result, for the use of the "B magic card" as well, there are also four options corresponding to two card placement areas CP × two placement methods. And adding the four options corresponding to the "A monster card" to these four options, a total of eight options form the second-layer option group.

[0073] When an effect is set for the "A monster card", further options are derived. For example, when a conditional effect such as placement by "summon" to the card placement area CP is set for the "A monster card", there are further two options for the placement method of "summon", namely "effect activation" and "no effect activation". Therefore, four options corresponding to these two options × two card placement areas CP are generated from the second-layer option group regarding the "A monster card".

[0074] Similarly, options also arise when two types of effects are set for the "B Magic Card". For example, when two types of effects, namely Effect A and Effect B, are set for the "B Magic Card", there are further two options for the placement method, namely "Activate Effect A" and "Activate Effect B". Therefore, four options corresponding to these two types of options × two card placement areas CP are generated from the second-layer option group related to the "B Magic Card". And adding the four options derived from the second-layer option group of the "A Monster Card" to these four options forms a third-layer option group consisting of a total of eight options. Regarding the Magic Card CO2, there may be various other options depending on the situation. For example, when the activation target of the effect can be specified, an option to select the target may further arise. Although not shown in the example of Figure 6, the same applies to other card objects CO such as the Trap Card CO3.

[0075] Furthermore, a fourth-layer option group is derived from the third-layer option group. For example, if two of the three Monster Cards CO1 already placed in the card placement area CP (for example, the "C Monster Card" and the "D Monster Card") can be the activation targets of the effect of the "B Magic Card", these two activation targets are derived as options. Therefore, eight options corresponding to these two activation targets × four options (two types of effects generated for each of the two card placement areas CP) form the fourth-layer option group.

[0076] In the example of FIG. 6, in the tree of option groups derived from the "A monster card", a part of the second-layer option group (when placed in the "set" state in each card placement area CP), and the third-layer option group correspond to the deepest-layer option group. Therefore, each option of these option groups is input into an artificial intelligence model such as an attack-type AI, and a probability value (the numerical value within the "○") is output for each option. Similarly, in the tree of option groups derived from the "B magic card", a part of the second-layer option group (when placed in the "set" state in each card placement area CP), and the fourth-layer option group correspond to the deepest-layer option group. Therefore, each option of these option groups is input into an artificial intelligence model such as an attack-type AI, and a probability value is output for each option.

[0077] On the other hand, there are no options derived from "phase transition" (phase end instruction) and "turn end" (turn end instruction), and for these, the first-layer option group corresponds to the deepest-layer option group. Therefore, the two options of "phase transition" and "turn end" are input into an artificial intelligence model such as an attack-type AI, and a probability value is output for each option. In the example of FIG. 6, as an example, the information of the probability value output by one type of artificial intelligence model among a plurality of artificial intelligence models is shown, but actually, for each option, the information of a plurality of probability values is output by each of the plurality of artificial intelligence models.

[0078] Also, in the example of FIG. 6, an example of a group of options in the battle phase is shown. In addition to the phase end instruction and the like, card objects CO to be placed from the hand CO5 to each card placement area CP, and options corresponding to the card placement areas CP where the card objects CO are to be placed are shown. For example, in the draw phase, options corresponding to the phase, such as adding (drawing) from the deck CO6 to the hand CO5, are prepared for each phase. Therefore, at least a part of the options that can be selected for each phase (for example, those at the end of the phase or at the end of the turn may be common) are different. That is, the options available in each phase are different. And information on the probability values of each option that can be selected for each phase is output. In this example, the group of options in the deepest layer functions as an example of the plurality of options of the present invention. Also, the group of options for each phase functions as two or more options of the present invention.

[0079] FIG. 7 is an explanatory diagram for explaining an example of a method by which an artificial intelligence model calculates probability values. Generally, an artificial intelligence model is generated by machine learning a learning data set using a predetermined learning method (algorithm), and tends to have a thought (algorithm) according to the learning method. The learning methods in machine learning include various methods. For example, imitation learning and reinforcement learning (imitation learning may be considered a type of reinforcement learning in some cases) are included. Imitation learning is often classified into methods such as Behavoir clonig, Dataset Aggregation, or Inverse Reinforcement Learning, but there are also many cases where the reward is not explicitly defined. On the other hand, reinforcement learning is often classified into methods such as Dynamic Programming (DP method, dynamic programming method), MC method (Monte Carlo method), or Temporal Differerence Learning (TD method), and is often a method that maximizes the reward.

[0080] The artificial intelligence model (analysis program AP) for analysis services may be appropriately generated by various learning methods. As an example, it is generated by the TD method among reinforcement learning methods. Also, the TD method may include techniques such as SARSA, etc. As an example, the Q-Learning (Q learning) technique is used for generating the artificial intelligence model for analysis services.

[0081] Q learning is a type of technique that evaluates actions (options) by obtaining the action value function (Q function). The Q function is generally defined as a function that predicts how the future reward (often called the Q value) will be when a certain action is taken in a certain state (the explanation of the specific function formula is omitted). However, in Q learning, when the Q function table (input information) becomes large (enormous), the calculation cannot keep up and it tends to be difficult to implement. On the other hand, for example, the input information (information of the situation log SL) on the battle screen 50 is considered to be enormous. For this reason, the learned artificial intelligence model for analysis services is generated by the DQN (Deep Q Network) technique that obtains an approximate value of the Q value using a neural network. In this case, the artificial intelligence model for analysis services is configured to calculate the probability value (Q value) using the DQN technique.

[0082] Also, the reward (a predetermined result obtained by an action, which may be included in the algorithm of the learning program) for which the Q value is the target may be appropriately set. For example, specific winning methods such as winning by a narrow margin or trends of winning methods may be set. The artificial intelligence model for analysis services may have different thoughts depending on the reward (predetermined result) in Q learning. As an example of the reward, winning in a battle is set, and the artificial intelligence model for analysis services is configured to calculate the probability value (Q value) for winning in a battle using the DQN technique.

[0083] In the example of Fig. 7, an overview of the DQN method for calculating probability values (Q-values) is shown. As shown in Fig. 7, in the DQN method, since a neural network (deep learning) is used, an input layer, an intermediate layer, and an output layer are formed, and each layer cooperates to output a calculation result for the data. The input layer is a layer that is responsible for collecting data. The intermediate layer is a layer that performs calculations for calculating probability values. The intermediate layer often includes multiple layers (generally, the more layers there are, the higher the accuracy tends to be). In the example of Fig. 7, only two intermediate layers are shown, but an appropriate number of intermediate layers may be formed. The output layer is a layer that outputs the calculation result performed in the intermediate layer. The artificial intelligence model learned by the DQN method is configured to output a probability value of winning in a battle at the output layer. Also, between each of the input layer, the intermediate layer, and the output layer, connection lines (often called synapses) that connect the layers are provided. And each connection line is provided with a value of weight (often represented by the sign of w) indicating importance, and the importance of information is determined by the magnitude of the weight value. In the example of Fig. 7, both the input value (input information) and the output value (sometimes generally called a node) are represented by "○".

[0084] Specifically, first, information of the situation log SL indicating the current state of the battle screen 50 is input to the input layer. The situation log SL includes information of many dimensions (input values). It is desirable that the number of information in this dimension is less than about 5000. The situation log SL may include various information for discriminating the situation of the card game as described above. For example, in addition to information on the placement situation in the field area 54 such as the situation of the 1st card placement area CP to the 10th card placement area CP, information on possible options such as turn end, summoning an A monster card, activating an A monster card effect, and activating a B magic card (see the example of Fig. 6 for example) is included.

[0085] A predetermined functional expression using appropriate weighting (weight value w) is applied to the input values in the input layer, and the output values of the intermediate layer (first layer) are calculated by that functional expression. An appropriate number of output values can be calculated for the intermediate layer of the first layer, but in the example of FIG. 7, four output values are calculated. Further, a predetermined functional expression using appropriate weighting is applied to the four output values, and the output values of the intermediate layer (second layer) are calculated by that functional expression. An appropriate number of output values can also be calculated for the intermediate layer of the second layer, but in the example of FIG. 7, four output values are still calculated. Note that the weighting (weight value w) corresponding to each connection line is calculated as a learning result and is managed, for example, in the analyzed learned data AD for analysis. Also, the connection lines (synapses) connecting the input layer and the intermediate layer are appropriately omitted in FIG. 7.

[0086] The output values of the final intermediate layer (second layer) are calculated as Q values by a predetermined functional expression using appropriate weighting in the output layer. The Q values (output values of the output layer) are calculated for each predetermined action (options on the battle screen 50). For this reason, for example, in each tree in the example of FIG. 6, the Q values are calculated by the method in the example of FIG. 7 for each option (selectable option) in the option group of the deepest layer. Also, the calculated Q values are converted into probability values by a normalization function (for example, the Softmax function) so that the sum of the values of all selectable options becomes 100%. As an example, probability values are calculated for all selectable options for each situation by such a method. Also, the same calculation is executed by each of a plurality of artificial intelligence models such as an attack-type AI or a defense-type AI (for example, the first analysis program AP1 and the second analysis program AP2). Then, the top (for example, five in the example of FIG. 5) of the plurality of probability values calculated by the plurality of artificial intelligence models are provided to the user via the advice display column 60. Note that the application of the normalization function is not essential. For example, when various conditions such as the learning method are different, the application of the normalization function may be appropriately omitted.

[0087] (Inference Service) Next, with reference to FIG. 8, the details of the speculation service will be described. The speculation service is a service that provides speculation information for speculating on the content of elements of an opponent that are not shared with the user among a group of elements (for example, card object CO) prepared for each player in a card game. The speculation service can be applied to an appropriate situation including elements hidden from the user. For example, it is applied to the speculation of card objects CO such as card objects CO placed face-down in the hidden field area 54 on the battle screen 50, hand cards CO5, draw pile CO6, or summoned deck cards CO7. That is, on the battle screen 50, speculation information for speculating on the content of card objects CO placed face-down and the like is provided via the speculation service. FIG. 8 schematically shows an example of the battle screen 50 when the speculation service is applied. In the example of FIG. 8, the same components as those of the battle screen 50 in FIG. 3 are denoted by the same reference numerals and their description is omitted.

[0088] As shown in FIG. 8, when the speculation service is applied, a speculation information section 70 is additionally displayed on the battle screen 50 as compared with the example of FIG. 3. The speculation information section 70 is a part for displaying speculation information. The speculation information section 70 can be configured as appropriate, but in the example of FIG. 8, it includes a target designation section 71, candidate images 72, and candidate arrangement lines 73. The target designation section 71 is a part indicating the target (speculation target) for displaying speculation information. The target designation section 71 may be configured as appropriate as long as it can allow the user to identify the target. In the example of FIG. 8, it is indicated by a dashed line surrounding the card object CO (card object CO with the front side hidden) that is the speculation target. Also, the speculation information section 70 may be displayed so as to make an appropriate card object CO the speculation target. For example, it may be displayed so as to target all face-down card objects CP, or it may be displayed so as to target an appropriate part of them. In the example of FIG. 8, it is displayed so as to target the face-down card object CP designated (selected) by the user. In this case, the target designation section 71 has a function of distinguishing the card object CP designated as the speculation target by the user from other face-down card objects CP.

[0089] The candidate image 72 is an image showing candidates for the card object CO to be estimated. More specifically, an image of the front side (contents) of the card object CO that is estimated to correspond to the card object CO to be estimated is displayed as the candidate image 72. Only the card object CO with the highest probability as the candidate image 72 may be displayed, but in the example of FIG. 8, two candidate images 72 (“A” and “B”) corresponding to the top two with high probabilities (an example and may be an appropriate number) are displayed. Also, a numerical value indicating the probability (hereinafter sometimes referred to as a matching probability value) is displayed above each candidate image 72. For example, a numerical value of “56%” is displayed above the candidate image 72 corresponding to “A”, and a numerical value of “35%” is displayed above the candidate image 72 corresponding to “B”. These matching probability values are output by a learned artificial intelligence model (estimation program FP) for the estimation service based on the situation log SL.

[0090] The candidate array line 73 is a line showing the relevance between the target specifying unit 71 and each candidate image 72. Each candidate image 72 is arranged along the line 73 below the candidate array line 73. Therefore, the candidate array line 73 also functions as a reference for arranging the candidate images 72 side by side horizontally.

[0091] (Calculation method of estimation information) Next, with reference to FIG. 9, a method for calculating inference information (candidates and match probability values) output by a learned artificial intelligence model for an inference service will be described. FIG. 9 is an explanatory diagram for explaining an example of a method by which an artificial intelligence model calculates inference information. The example of FIG. 9 shows a case where inference information is provided on the battle screen 50. The artificial intelligence model for the inference service may also be obtained by an appropriate learning method, but the example of FIG. 9 shows a case where it is generated by a neural network (deep learning) method. As shown in FIG. 9, in this case, similar to the calculation of probability values, inference information is calculated using an input layer, an intermediate layer, and an output layer. Specifically, in the input layer, information indicating the situation of the battle screen 50 is input as input information (input values) into the learned artificial intelligence model for the inference service. Although information different from the case of calculating probability values may be used as the input information, as an example, a similar situation log SL (only a part is shown in FIG. 9) is used.

[0092] In the intermediate layer, an output value is calculated from the input value in the input layer using a predetermined functional formula that utilizes appropriate weighting (weight values). However, the weight values and the functional formula used therein are different from those of the artificial intelligence model for the analysis service. The weight values are defined by the inference learned data FD (data generated by the above learning), and the functional formula is configured to infer the content of the backside-facing card object CO. Although an appropriate number of intermediate layers can be formed in the calculation of inference information, two intermediate layers are formed in the example of FIG. 9. Similarly, an appropriate number of output values (nodes) can be provided in each intermediate layer, but in the example of FIG. 9, four output values are provided in each intermediate layer.

[0093] In the output layer, a value indicating the possibility corresponding to the card object CO to be inferred is calculated for each card object CO from the output value of the final intermediate layer (the second layer). The card objects CO for which the possibility is calculated may be all types that can be used in the card game (the opponent's deck cards may not be considered), but as an example, it is limited to the card objects CO incorporated in the opponent's deck cards. This limitation may be realized as appropriate. For example, when a learning dataset that includes only the opponent's deck cards as performance samples in advance is prepared and a learned artificial intelligence model (inference model unit 24) that has learned the learning dataset is provided, it may be limited by using the artificial intelligence model. As an example, it is realized by excluding (filtering) cards other than the opponent's deck cards from the candidates after calculating the inference candidates.

[0094] Also, in the card game, the rules are reflected as appropriate. For example, there may be a rule that limits the number of cards of a specific type of card object CO that can be incorporated into the deck to three. In this case, if all three cards of that specific type of card object CO have already been opened (for example, if they are placed face-up in the field area 54, etc.), that specific type of card object CO is excluded (filtered) from the target of the inference information.

[0095] In addition, rule modifications may occur as appropriate at a predetermined time (including regular times such as once a year, and temporary times for responding to situations at any time, etc., non-regular times). For example, the number of a specific type of card object CO that can be incorporated into the deck cards may change from three to two, etc., and the available number may change. Similarly, regulation changes such as limit regulations (restrictions that make a specific card object CO unusable) may be executed as appropriate. For example, if there is a card object CO that is too powerful, there is a possibility that the deck cards may become biased due to that card object CO always being incorporated, etc. Or, there may be a feeling of unfairness in the battle results. Therefore, for example, when a specific card object CO is prohibited from being used, that specific card object CO is excluded (filtered) from the objects for which possibilities are calculated. Or, when the number of uses is changed to two, when both become open, that card object CO is excluded from the target of the speculation information (when the limit number increases, the exclusion from the target is postponed until it corresponds to the limit number).

[0096] In a card game, as described above, various changes such as changes in numbers or types can occur in the available card objects CO (elements) according to rule modifications, etc. And that change is reflected in the target of the speculation information. Specifically, the range in which a value indicating a possibility is calculated (in other words, the card objects CO that correspond to the candidates for the hidden card objects CO) is limited to the card objects CO that are available under the current rules and that the opponent has incorporated into the deck cards.

[0097] Also, in the output layer, the values indicating possibilities (the values of each candidate after being limited to candidates that can be used by the filter) are normalized such that the sum of the values of all options that can be selected by a normalization function (e.g., the Softmax function) (e.g., all card object COs incorporated by the opponent into the deck cards) becomes 100%. Then, the value after such normalization is calculated as inference information (coincidence probability value). As an example, for each card object CO (card object CO whose content is hidden) to be inferred by such a method, the candidate card object CO and the coincidence probability value are calculated by a learned artificial intelligence model for the inference service. Then, the top (e.g., the top two in the example of FIG. 8) of the calculated coincidence probability values among the candidate card object COs is provided to the user via the inference information unit 70. Also, when the information input in the input layer includes executable actions (options), even if the situation is the same, how it was used to reach the current situation (board state) is considered. Thereby, it is possible to realize the calculation of highly accurate inference information.

[0098] (Type of inference information) Next, with reference to FIGS. 10 to 11, the types of inference information that can be provided via the inference service will be described. The learned artificial intelligence model for the inference service may be only one type, but as an example, a plurality of types of artificial intelligence models (for example, the first inference program FP1, the second inference program FP2, etc.) are prepared. Each artificial intelligence model outputs inference information based on different learned inference data FD for inference. Since different artificial intelligence models may reach the same conclusion, that is, the same inference information, it is not always the case that different inference information is output by a plurality of types of artificial intelligence models, but in many cases, a plurality of types of inference information are output. FIG. 10 is an explanatory diagram for explaining an example of the types of inference information output by a plurality of types of artificial intelligence models. Also, various inference information can be appropriately output by each artificial intelligence model, but in the example of FIG. 10, (1) recommendation information for each period and (2) recommendation information for each rank are shown. Furthermore, appropriate recommendation information (including all of them) among these plurality of recommendation information may be displayed as a display target, but in the example of FIG. 10, only a part of the recommendation information selected by the user is displayed in both (1) and (2).

[0099] The recommendation information for each period is the recommendation information output by a learned artificial intelligence model based on the learned inference data FD for inference generated by the learning data set for each period. In this case, the plurality of learned inference data FD are each generated based on a plurality of learning data sets (each including performance samples for a plurality of periods). The inference information for each period may be provided as appropriate, but as an example, it is switched by a user's instruction (selection). The user's instruction may be executed as appropriate, but in the example of FIG. 10, the case of execution via the information switching unit 80 is shown.

[0100] On the one hand, the recommended information for each rank is the recommended information output by a learned artificial intelligence model based on the learned inference data FD for each rank generated by the learning dataset for each rank. The card game is provided with a function of ranking players according to their achievements (information indicating the skill level of the players), and the recommended information for each rank corresponds to the output result of the artificial intelligence model whose learning dataset includes performance samples of the same rank. In this case, a plurality of learned inference data FDs corresponding to a plurality of learning datasets (each including performance samples of a plurality of ranks) are prepared. The inference information for each rank may be provided as appropriate, but as an example, it is provided so as to be switched according to the user's instruction via the information switching unit 80, similar to the inference information for each period. In the example of FIG. 10, in both (1) and (2), the inference information unit 70 in the case where the battle screen 50 includes the information switching unit 80 is enlarged and schematically shown.

[0101] As shown in (1) and (2) of FIG. 10, the information switching unit 80 includes an information type unit 81, a first switching unit 82, and a second switching unit 83. The information type unit 81 is a part for displaying the type of the inference information that is currently the display target. Both the first switching unit 82 and the second switching unit 83 are parts indicating positions where an instruction (a touch operation as an example) for switching the type of the inference information to be displayed should be executed. When a touch operation is executed on the first switching unit 82 and the second switching unit 83, the current display target is switched to the previous display target and the subsequent display target, respectively.

[0102] Specifically, as shown in (1) of FIG. 10, when the estimated information for each period is provided, the type of the estimated information is expressed by the information of "23.1 - 23.2" indicating the period as an example. The information of "23.1 - 23.2" (indicating a one - month period from January to February 2023) indicates the period during which the performance samples included in the generated learning dataset were collected. On the other hand, when a touch operation is executed on the first switching unit 82, the display target is switched to a period before the currently displayed target period (for example, a one - month period from December 2022 to January). That is, the display target is switched to the estimated information (candidate image 72 including a coincidence probability value) output by the artificial intelligence model generated by a plurality of learning datasets each including the performance samples of the previous period. Similarly, when a touch operation is executed on the second switching unit 83, the display target is switched to a period after the currently displayed target period (for example, a one - month period from February to March 2023). In the example of FIG. 10, the range of the display target is divided monthly, but it is not limited to this and may be appropriate as needed.

[0103] Also, as shown in (2) of FIG. 10, when the estimated information for each rank is provided, the type of the estimated information is expressed by the information of "the second rank" indicating the rank. The information of "the second rank" indicates the rank of the player in which the performance samples included in the generated learning dataset were collected. In this case, it means that a plurality of learning datasets each including the performance samples only of the players of the second rank are the generation sources. On the other hand, when a touch operation is executed on the first switching unit 82, the display target is switched to a rank lower than the currently displayed target rank (for example, the first rank). That is, the display target is switched to the estimated information (candidate image 72 including a coincidence probability value) output by the artificial intelligence model generated by a plurality of learning datasets each including the performance samples only of the players of the first rank, which is one rank lower than the current second rank. Similarly, when a touch operation is executed on the second switching unit 83, the display target is switched to a rank higher than the currently displayed target rank (for example, the third rank).

[0104] In the example of FIG. 10, the display target range is divided for each rank, but it is not limited to this, and it may be an appropriate range (for example, two or more ranks, etc.). In that case, information indicating the range may be added. Also, the initial display target (default display) may be set as appropriate, and may be set, for example, to the user's own rank, or the rank of the opponent in the game. In this case, since the speculation information adjusted to the user's own or the opponent's rank is displayed by default, the usefulness can be improved. Similarly, the initial display during a period may be set as appropriate, and may be set, for example, to the latest period, or the period with the highest usage frequency, etc.

[0105] Each artificial intelligence model can output various types of speculation information based on differences such as algorithms. As described above, in the example of FIG. 10, a plurality of types of speculation information are output based on the differences in the learned data FD for speculation. In card games, there may be a trend in the types of card objects CO used (combinations of deck cards). If there is a trend, the artificial intelligence model that reflects the trend is more likely to output more accurate speculation information. And trends often occur for each period. Therefore, when speculation information for each period is provided, the trend can be reflected in the speculation information, and thus more accurate speculation can be realized. Similarly, in card objects, there are often differences in the card objects CO incorporated into the card deck and the usage methods, etc., according to the rank (the skill level of the player). Therefore, when speculation information for each rank is provided, the usage tendency for each rank can be reflected in the speculation information, and thus more accurate speculation can be realized.

[0106] Note that it is not limited to the learning datasets for each of the above-mentioned periods or the learning datasets for each rank. As multiple learning datasets, appropriate learning datasets including achievements collected from various perspectives such as rules (regulations), the number of available cards, etc. may be used. Therefore, for example, a learning dataset including achievement samples regarding all card objects CO (not limited by period or rank) included in a card game may be used. That is, the multiple inference-use learned data FDs may be data that gives appropriate thinking to an inference program, generated by various learning datasets.

[0107] FIG. 11 is an explanatory diagram for explaining learning datasets for realizing inference information for each period and each rank. The inference information for each period and each rank may be prepared separately as independent inference information. For example, it may be realized by separate learning datasets such as multiple learning datasets for each period or multiple learning datasets for each rank. On the other hand, inference information considering both, such as inference information for each period being prepared for each rank, may be prepared. The example of FIG. 11 shows the types of learning datasets in the case where inference information considering both is prepared.

[0108] As shown in FIG. 11, in the case where inference information considering both is prepared, learning datasets of types corresponding to the number of periods × the number of ranks are prepared. Specifically, the learning dataset corresponding to the first rank (including achievement samples of players of the first rank) is prepared for each period, such as the "first learning dataset" corresponding to the period of "2023.1 - 2023.2" and the "second learning dataset" corresponding to the period of "2023.1 - 2023.2" (both learning datasets include achievement samples of players in the corresponding period). In this case, for example, the artificial intelligence model generated by the "first learning dataset" outputs inference information considering (reflecting) both the tendencies of players of the first rank and the tendencies of deck cards that were popular from January to February 2023.

[0109] The same applies to the second rank and the third rank, and learning data sets for each period such as the "third learning data set" to the "sixth learning data set" are prepared respectively. And the artificial intelligence models based on them output speculation information considering (reflecting) the trends of each rank and each period. In the example of FIG. 11, the first rank to the third rank are shown, but the card game may be provided with an appropriate number of ranks, and learning data sets corresponding to the number may be prepared. The same applies to the period.

[0110] (Processing of Network System) Next, with reference to FIGS. 12 to 14, as an example of the processing of the network system 1, the option analysis processing, the display change processing, and the backside speculation processing will be described. The option analysis processing is a process for providing a plurality of types of superiority and inferiority information for each option of the selection opportunity by using a plurality of types of artificial intelligence models. The option analysis processing may be configured to provide a plurality of types of superiority and inferiority information by using a plurality of types of artificial intelligence models in parallel, but the example of FIG. 12 shows a case where a plurality of types of artificial intelligence models are used serially to provide a plurality of types of superiority and inferiority information. Further, the option analysis processing is realized by the cooperation of the analysis model unit 23 of the game server 2 and the model processing unit 35 of the user device 3. In the example of FIG. 12, the process mainly executed by the analysis model unit 23 is shown as the game server 2, and the process mainly executed by the model processing unit 35 is shown as the user device 3, respectively.

[0111] When a predetermined analysis time (for example, the user's turn in the battle screen 50 or each phase) arrives, the model processing unit 35 starts the option analysis process of FIG. 12 and first requests the game server 2 to analyze each option in the selection opportunity (step S101). This request may be executed as appropriate. For example, it may be executed for the analysis model unit 23, and in the analysis model unit 23, the analysis results of each analysis model unit 23 such as the first analysis model unit 23A and the second analysis model unit 23B may be output in a predetermined order. However, as an example, it is executed for any one of the analysis model units 23 such as the first analysis model unit 23A and the second analysis model unit 23B in a predetermined order. Specifically, in step S101, the model processing unit 35 first requests the first analysis model unit 23A to analyze each option. Further, this request includes a situation log SL indicating the situation at the time of the request.

[0112] When the analysis request is sent, the first analysis model unit 23A starts the option analysis process of FIG. 12 and first obtains the request (step S201). Subsequently, the first analysis model unit 23A executes the analysis of each option based on the situation log SL included in the obtained request (step S202). This analysis may be executed only for some of the options in the selection opportunity, but as an example, it is executed for all options. When the analysis is executed, the first analysis model unit 23A outputs the analysis result (step S203) and sends it to the model processing unit 35 (step S204). Then, after the transmission, the first analysis model unit 23A ends the current option analysis process.

[0113] On the one hand, when the model processing unit 35 receives the analysis result from the first analysis model unit 23A, it acquires the analysis result and determines whether the number of types of the acquired analysis result (in other words, the artificial intelligence model) has reached the set number (for example, two types: an attack-type AI and a defense-type AI) (step S102). If the number of types of the acquired analysis result has not reached the set number (step S102: No), the model processing unit 35 returns to step S101 and requests the next analysis model unit 23 (the second analysis model unit 23B) to perform analysis in a predetermined order (step S101).

[0114] When the analysis request is sent, the second analysis model unit 23B starts the option analysis process shown in FIG. 12, and first acquires the request (step S201). Then, similar to the first analysis model unit 23A, it executes the analysis of each option (step S202), outputs the analysis result (step S203), and sends it to the model processing unit 35 (step S204). After the transmission, the second analysis model unit 23B ends the current option analysis process.

[0115] On the other hand, when the number of types of the acquired analysis result has reached the set number (step S102: Yes), the model processing unit 35 extracts the analysis result (superiority and inferiority information) to be displayed from all the acquired analysis results (including the analysis results of multiple types of analysis model units 23) (step S103). The analysis result to be displayed can be set as appropriate. As an example, it is set to the analysis result showing the top five probability values across the analysis results by multiple types of analysis model units 23. Therefore, in step S103, the model processing unit 35 extracts the analysis results corresponding to the top five probability values among all the analysis results as the display targets. Subsequently, the model processing unit 35 displays an advice display column 60 including the five analysis results extracted in step S104 as the advice section 61 on the battle screen 50. After this display, the model processing unit 35 ends the current option analysis process. Thereby, the analysis service on the battle screen 50 is realized via the advice display column 60. More specifically, a battle screen 50 including an advice display column 60 that carefully selects and provides the top five pieces of information on the probability values output from multiple artificial intelligence models for each option is realized.

[0116] The display change process is a process for changing the display mode of each analysis result in the advice display column 60 such as the display target or the order. The display mode of the advice display column 60 may be fixed, for example, as five in the order of the top probability values, but as an example, it is variable and changes according to the user's designation. When the change of the display mode of the advice display column 60 is instructed by the user, the model processing unit 35 starts the display change process of FIG. 13 and first discriminates the conditions designated by the user (step S301). Although arbitrary designations may be allowed for the user, as an example, they are limited to the conditions prepared in advance. Also, the conditions prepared in advance may include appropriate conditions, but for example, include a filter condition and a sort condition.

[0117] The filter condition is a condition for limiting a part of the analysis results of the display target. The filter condition may be various conditions for appropriately limiting the display target, but for example, includes the case of limiting to the analysis results of a specific artificial intelligence model (when three or more types of analysis model units 23 are prepared, two or more types of analysis model units 23 may also be used) based on the user's designation. The sort condition (sorting condition) is a condition for sorting the analysis results in a predetermined order designated by the user. For this reason, in step S301, the model processing unit 35 discriminates conditions such as the filter condition or the sort condition as the designated conditions. In the case of the filter condition, the model processing unit 35 also discriminates the specific artificial intelligence model designated as the filter target as the designated condition. On the other hand, in the case of the sort condition, the model processing unit 35 discriminates the predetermined order designated as the sort condition as the designated condition. Both the filter condition and the sort condition may be designated, and in that case, both are discriminated.

[0118] Subsequently, the model processing unit 35 determines the analysis result (superiority / inferiority information) of the target corresponding to the specified condition determined in step S301 from all the analysis results output by the analysis model unit 23 (all the analysis results obtained in step S102 of the option analysis process in FIG. 12) (step S302). Specifically, the model processing unit 35 determines the analysis result output by a specific artificial intelligence model (a specific type of the analysis model unit 23 such as the first analysis model unit 23A or the second analysis model unit 23B) specified as a filter condition among all the analysis results. Alternatively, the model processing unit 35 determines each analysis result corresponding to the order of a predetermined order. The predetermined order may allow appropriate designation. For example, the order for each artificial intelligence model such as the second analysis model unit 23B (defensive AI), the first analysis model unit 23A (offensive AI), the order in ascending order of probability values, the order in descending order of probability values within a predetermined range, or the order in ascending or descending order of the total value of probability values by a plurality of artificial intelligence models (any appropriate order such as ascending or descending) may be allowed.

[0119] Next, the model processing unit 35 changes the display of the advice display column 60 so as to display the analysis result of the target determined in step S302 according to the specified conditions (step S303). For example, when the analysis result of the first analysis model unit 23A (for example, attack-type AI) is specified as the filter condition, the display of the advice display column 60 is changed so that only each analysis result of the first analysis model unit 23A is displayed. When the sorting order for each artificial intelligence model is specified as the sorting condition, the display of the advice display column 60 is changed so that the advice parts 61 are arranged for each artificial intelligence model. The same applies when the sorting order such as in ascending order of the low probability value, in descending order of the probability value within a predetermined range, or in descending order of the total value of the probability values by a plurality of artificial intelligence models is specified. In these cases, the number of display targets may change appropriately, but as an example, it is set to five, the same as before the change. For this reason, the model processing unit 35 changes the display of the advice display column 60 so as to display the advice part 61 corresponding to the top five probability values of the first analysis model unit 23A or the top five probability values from the top in the specified sorting order, respectively. Then, after this change, the model processing unit 35 ends the current display change process. As a result, the display of the advice display column 60 is changed so as to display the analysis result specified by the user in the specified sorting order or the like.

[0120] The backside speculation process is a process for providing speculation information regarding the card object CO (information not shared with the user) facing the backside of the opponent in the battle screen 50 by using a learned artificial intelligence model. The backside speculation process is realized by the cooperation of the speculation model unit 24 of the game server 2 and the model processing unit 35 of the user device 3. In the example of FIG. 14, the process mainly executed by the speculation model unit 24 is shown as the game server 2, and the process mainly executed by the model processing unit 35 is shown as the user device 3, respectively.

[0121] When a predetermined start condition is satisfied (for example, when a user touches and selects a card object CO facing the back side on the battle screen 50), or when a change in the type of speculation information is instructed via the first switching unit 82 or the like of the information switching unit 80, the model processing unit 35 starts the back side speculation process of FIG. 14. First, it requests the game server 2 to speculate on the card object CO facing the back side of the target on the battle screen 50 (the card object CO selected by the touch operation or the card object CO being selected before the switching instruction) (step S401). This request includes a situation log SL indicating the situation at the time of the request. When a change in the type of speculation information is instructed via the first switching unit 82 or the like of the information switching unit 80, the request also includes information on the type of speculation information specified by the instruction.

[0122] When the speculation request is sent, the speculation model unit 24 starts the back side speculation process of FIG. 14 and first acquires the request (step S501). Subsequently, the speculation model unit 24 executes speculation on the content of the card object CO facing the back side based on the situation log SL included in the acquired request (step S502). When a change in the type of speculation information is instructed via the first switching unit 82 or the like of the information switching unit 80, this speculation is executed by a speculation model unit 24 of the type corresponding to the instructed type, such as the first speculation model unit 24A and the second speculation model unit 24B. Then, the speculation model unit 24 outputs the speculation result of the specified artificial intelligence model such as the first speculation model unit 24A (step S503). In this case, the speculation model unit 24 may output the speculation result only for the number (for example, the top two) to be displayed as the candidate image 72, but as an example, it outputs to include information on all candidates. Also, the speculation model unit 24 may output speculation information regarding all of the card objects CP facing the back side included in the battle screen 50 as the speculation result (in this case, when an object other than the current specified target is newly specified as the speculation target later, the user device 3 may extract and display the speculation information of the newly specified target from the already output speculation information), but as an example, it outputs only the speculation information regarding the card object CP facing the back side specified this time.

[0123] Next, the inference model unit 24 adjusts the inference result (step S504). The adjustment can be realized as appropriate. As an example, it is executed to reflect rules such as limit regulation and the opponent's deck cards. For example, the inference model unit 24 adjusts the inference result so as to exclude the card object CO that has been prohibited from use and the card object CO that the opponent has not incorporated into the deck cards from the output inference result. Also, this exclusion may be executed by simply excluding the card object CO such as prohibited from use from the candidates output as the inference result. As an example, it is executed so that the coincidence probability value is recalculated (adjusted) after the exclusion. After that, the inference model unit 24 transmits the adjusted inference result to the model processing unit 35 as inference information (step S505). Then, after the transmission, the inference model unit 24 ends the current backside inference process. Note that when the process is executed by an artificial intelligence model that has learned the learning dataset limited to the adjusted target as described above (when the same result can be obtained without adjustment), etc., in the case where adjustment is not required (including the case where adjustment is not executed at all), the process of step S504 may be omitted as appropriate.

[0124] On the other hand, when the inference information is transmitted from the inference model unit 24, the model processing unit 35 acquires the inference information (step S402). Subsequently, the model processing unit 35 specifies the inference information to be displayed from the acquired inference information (step S403). For example, when the number of candidates to be displayed as the candidate image 72 in the inference information unit 70 is the top two in terms of the coincidence probability value, the inference information corresponding to the top two in terms of the coincidence probability among the acquired inference information is specified as the display target. Next, the model processing unit 35 displays the inference information of the display target specified in step S404 on the battle screen 50 (step S404). Specifically, the inference information unit 70 including the inference information specified in step S404 is displayed on the battle screen 50. Then, after this display, the model processing unit 35 ends the current backside inference process.

[0125] According to the procedure of FIG. 14, the speculation service on the battle screen 50 is realized via the speculation information unit 70. More specifically, as information for speculating on the content of the back-facing card object CO, speculation information output from an artificial intelligence model (for example, information including the content of the candidate card object CO and the matching probability) is strictly selected and provided for the top two for each back-facing card object CO. A battle screen 50 including a speculation information unit 70 is realized. Note that the display target or the order of the speculation information unit 70 may be appropriately changed. In that case, for example, the display target and the like may be changed according to the same procedure as the display change process in the example of FIG. 13 (however, the procedure targets the speculation information and the speculation information unit 70 instead of the analysis result and the advice display column 60).

[0126] As described above, according to this form, via the analysis service, a plurality of probability values (superiority and inferiority information) respectively indicating the probability of winning the battle for each option on the battle screen 50 by a plurality of trained artificial intelligence models (the first analysis model unit 23A, the second analysis model unit 23B) (which may be information indicating various possibilities of progressing advantageously within a predetermined range of the analysis target) are output, and information on the top five of these probability values is provided before the selection of the option. Therefore, in order to win the battle, for each option of selection such as which card object CO should be placed in which card placement area CP or whether the phase should be ended, it is possible to provide information on the probability value (superiority and inferiority information), that is, numerical information indicating the magnitude of the influence on winning in the game, from various perspectives.

[0127] Also, in a card game, turns including a plurality of phases are alternately provided to players, and a selection opportunity is provided for each phase. However, the end of a phase or the end of a turn is also included in the group of options in each selection opportunity. When the alternation of turns or the like functions as one of the options, the influence of the selection in the selection opportunity on the progress tends to become more complicated. When each turn includes a plurality of selection opportunities through a plurality of phases and each of those selection opportunities selectively ends, it is highly likely to become even more complicated. Originally, in each selection opportunity, a complicated selection including many options (including options further derived from each option) is required. Therefore, even if the turn instruction or the like is not selective, a complicated selection is still required. Regarding such a complicated selection, information on a plurality of probability values can be provided from various viewpoints. Further, the information on the plurality of probability values is provided with some being limited through a filter condition or the like, or being rearranged as appropriate. For this reason, it is possible to improve the convenience of accessing information on a specific probability value (option information) through a filter condition or the like.

[0128] On the other hand, through the speculation service, information on the card object CO (element) of the opponent that is not shared with the user, for example, the content of the card object CO hidden from the user by being placed face down, is speculated by a learned artificial intelligence model (speculation model unit 24), and the speculation result is provided as speculation information. Therefore, the speculation information can assist in speculating on the card object CO hidden in the imperfect information game.

[0129] Also, in a card game, among all card objects CO, the deck cards selected by the user from the owned cards are used by the user. Therefore, a card game is configured as a competitive game of a type where there are differences in assets between players. In this case, it is highly likely that it becomes more difficult to infer the card objects CO that are hidden compared to the case where all element groups are used. Similarly, in a card game, the card objects CO that can be used may change depending on the timing and the like, and it is also highly likely that it becomes more difficult to infer the card objects CO that are hidden compared to the case where they are fixed (do not change). For this reason, an experienced player may be able to infer the content of the card objects CO hidden, for example, by looking at the back side, based on rules of thumb, but generally such inference is difficult, and it is often particularly difficult for beginners. Even when it is difficult to infer the hidden card objects CO like this, the inference can be assisted by inference information. Therefore, it is possible to help bridge the gap with experts (eliminate the gap) by the inference information. And when the change in the usable card objects CO is reflected in the inference information, the accuracy of the inference information can be improved compared to the case where the same change is not reflected. Therefore, it is possible to further help bridge the gap with experts by the inference information.

[0130] Furthermore, a plurality of pieces of inference information are provided by a plurality of learned artificial intelligence models according to the time period and rank. In card games, there may be trends in the types (element groups) of card objects used. If there is a trend, the artificial intelligence model that reflects the trend is more likely to output more accurate inference information. And trends often occur for each period. Similarly, there are often differences according to the rank (the skill level of the user) in the types and usage methods of the card objects CO incorporated in the group of card objects used. Therefore, by training a learning dataset including performance for each predetermined period, the trend for each period can be reflected in the inference information. Alternatively, by training a learning dataset including performance for each rank, the usage tendency for each rank can be reflected in the inference information. By these means, more accurate inference can be realized. Furthermore, the type of inference information (the target period and rank) can be switched according to the user's selection. For this reason, it is possible to select the type of inference information according to the opponent (whether the opponent is a beginner or which period's trend the opponent's deck cards correspond to, etc.) (in other words, the user's inference about the opponent can be reflected in the inference information), so the accuracy of the inference information can be further improved.

[0131] In the above form, the first analysis model unit 23A and the second analysis model unit 23B of the game server 2 (or the processes in FIG. 12 executed by them respectively) function as a plurality of types of processes of the present invention. On the other hand, the model processing unit 35 of the user device 3 functions as the superiority-inferiority acquisition means and the superiority-inferiority providing means of the present invention by executing the procedure in FIG. 12. Specifically, the model processing unit 35 functions as the superiority-inferiority acquisition means by executing step S102 in FIG. 12 and functions as the superiority-inferiority providing means by executing step S104. Also, the model processing unit 35 of the user device 3 functions as the filtering means or the sorting means of the present invention by executing the procedure of step S303 in FIG. 13.

[0132] In addition, the estimation model unit 24 of the game server 2 (or the process of FIG. 14 executed by the same) functions as a predetermined process of the present invention. On the other hand, the model processing unit 35 of the user device 3 functions as the information acquisition means and the information providing means of the present invention by executing the procedure of FIG. 14. Specifically, the model processing unit 35 functions as the information acquisition means by executing step S402 of FIG. 14, and functions as the information providing means by executing step S404.

[0133] The present invention is not limited to the above-described forms, and may be implemented in a form with appropriate modifications or changes. Further, the present invention may be implemented in a form obtained by appropriately combining various technical means included in the above-described forms and forms with the following modifications and the like. In the above-described form, in the card game provided as a video game, superiority / inferiority information and estimation information are provided to the player. However, the present invention is not limited to such a form. For example, the superiority / inferiority information and the estimation information may be provided to the player or viewers watching the game in a game in the real space. Specifically, for example, in a game in the real space, the superiority / inferiority information or the estimation information may be provided by being added to a video (viewing video) of the game scene, or may be provided through sound to the real space where the game is played (including players and viewers).

[0134] In the above-described form, each analysis model unit 23 such as the first analysis model unit 23A (a learned artificial intelligence model for analysis service) and each estimation model unit 24 such as the first estimation model unit 24A (a learned artificial intelligence model for estimation service) are given different thoughts due to the difference in learned data such as the learned data AD for analysis or the learned data FD for estimation. However, the present invention is not limited to such a form. Each analysis model unit 23 and each estimation model unit 24 may be given different thoughts due to, for example, the difference in the algorithms of the learning program (reward in reinforcement learning such as Q-learning, that is, including a predetermined result) and the inference program.

[0135] In the above form, the superiority and inferiority information is provided via the advice display column 60. However, the present invention is not limited to such a form. The superiority and inferiority information may be provided as appropriate. FIG. 15 is an explanatory diagram for explaining an example of a modification regarding the display mode of the superiority and inferiority information. In the example of FIG. 15, two modifications are shown by (1) a first modification example and (2) a second modification example. As shown in FIG. 15(1), in the first modification example, the probability values of each analysis model unit 23 are provided via the target card image 90, the first analysis result unit 91, and the second analysis result unit 92.

[0136] The target card image 90 is an image showing the candidate card object CO as an option. The target card image 90 may be the card object CO itself or a thumbnail-like image substituting it. The first analysis result unit 91 and the second analysis result unit 92 are parts showing the types (types of analysis results) of the analysis model unit 23, such as the first analysis model unit 23A (for example, an attack-type AI) or the second analysis model unit 23B (for example, a defense-type AI). The first analysis result unit 91 and the second analysis result unit 92 may be formed as appropriate, but in the example of FIG. 15, both are formed in a rectangle surrounding the target card image 90 and are respectively shown by a dashed line and a thick solid line.

[0137] In addition, the first analysis result section 91 and the second analysis result section 92 are provided with a numerical information section 93 that shows the probability value (analysis result) by each analysis model section 23 such as "46.2". Furthermore, a total information section 94 may be provided. For example, in the first modification example of FIG. 15, three candidates are shown, and a total information section 94 is added to the middle candidate among them. The total information section 94 is a part that shows the total of the probability values of a plurality of types of artificial intelligence models for one candidate (option). The target card image 90 may be displayed for all candidates, but in the example of FIG. 15, it is displayed limited to a part such as the top three of the probability values. Since the probability values by a plurality of types of analysis model sections 23 are output for all three candidates, the total information section 94 may be displayed for all of them, but in the example of FIG. 15, it is displayed limited to a part (the middle target card image 90) of them. The display target of the total information section 94 can be set appropriately, but as an example, it is set to a candidate whose total value exceeds the highest probability value. For example, in the middle target card image 90, a numerical information section 93 showing "25.2" and "32.0" is provided in the first analysis result section 91 and the second analysis result section 92 respectively, and "57.2" showing the total value of them is shown as the total information section 94. And this total value is higher than any probability value of the other target card images 90. In such a case, the total information section 94 is provided.

[0138] Also, as shown in (2) of FIG. 15, in the second modification example, the probability value of each analysis model section 23 is provided via the target card image 90, the first gauge section 95, and the second gauge section 96. The target card image 90 is the same as in the first modification example. The first gauge section 95 and the second gauge section 96 are parts that show each analysis model section 23 (type of analysis result) such as the first analysis model section 23A, and are configured to visually show the magnitude of the probability value. The first gauge section 95 and the second gauge section 96 can be formed appropriately, but in the example of FIG. 15, both are formed in a rod shape extending upward from a predetermined position according to the magnitude of the probability value, and are shown in white and black color schemes respectively.

[0139] In addition, a detailed information section 97 is provided in both the first gauge section 95 and the second gauge section 96. The detailed information section 97 may contain various types of information. In the example of FIG. 15, it contains information on the type of the analysis model section 23 (such as "attack-type AI" or "defense-type AI") like the first analysis model section 23A (for example, attack-type AI) or the second analysis model section 23B (for example, defense-type AI), and information on probability values ("46.1" or "32.0"). Although omitted in the example of FIG. 15, a total information section 94 may be provided as appropriate, and information on the total value may be displayed. The superiority / inferiority information may be provided as appropriate, and is not limited to the example of FIG. 5, and may be provided, for example, as in the modified example of FIG. 15.

[0140] In the above-described form, the network system 1 includes the game server 2. However, the present invention is not limited to such a form. For example, in the case where an offline game played without connecting to the network NT is provided, etc., the game server 2 may be omitted, and the user device 3 may function alone as the game system of the present invention. In other words, various artificial intelligence models such as each analysis model section 23 may be provided in the user device 3. Therefore, for example, in the example of FIG. 12 or the example of FIG. 14, the processing (role) of the game server 2 may be executed by an artificial intelligence model (a logical device similar to the analysis model section 23, etc.) provided in the user device 3, which is called by the model processing section 35.

[0141] Alternatively, conversely, the game server 2 may execute all or part of the role (various processes, etc.) of the user device 3. For example, the learned data such as the learned data AD for analysis or the learned data FD for estimation may be stored in the game server 2, and the programs for the artificial intelligence model such as the analysis program AP or the estimation program FP may be provided in the user device 3. Also, data etc. necessary for the realization of the present invention, such as each part of the various learned data or each part of the program for the artificial intelligence model, may be appropriately and dispersedly recorded in the game server 2 and the user device 3. And the game system of the present invention may be realized by the cooperation between the game server 2 and the user device 3. The same applies to the game program PG2 and the game data GD. In these cases, the combination of the user device 3 and the game server 2 (for example, the network system 1), or the game server 2 alone (including the case where it is composed of a plurality of server devices), etc. may appropriately function as the game system of the present invention. And the program and the control method implemented in devices such as the network system 1, the user device 3, or the game server 2 may function as the game program and the control method of the present invention.

[0142] The various aspects of the present invention derived from each of the above-described embodiments and modification examples are described below. In the following description, for the sake of easy understanding of each aspect of the present invention, the corresponding members illustrated in the accompanying drawings are appended in parentheses, but the present invention is not limited to the illustrated forms by this.

[0143] The computer program of the present invention configures a computer (31) incorporated in a game system (3) that provides element information as information regarding at least one of the element groups (CO) for each user in a game played by a plurality of users using the element groups for each user, in an incomplete information game of a type in which the content of at least one of the element groups of other users provided as the game is not shared with the one user, to function as information acquisition means (35) for acquiring the inference information using a predetermined process (24) set so as to obtain inference information for inferring the content of the at least one element, and information providing means (35) for providing the inference information as the element information.

[0144] According to the present invention, the content of an element not shared with one user, that is, an element concealed from one user, is inferred by a predetermined process, and the inference result is provided as inference information. Therefore, the inference information can assist in inferring elements concealed in an incomplete information game. Note that the game of the present invention may be a game in the real space and does not necessarily need to be provided by a game system. Similarly, the predetermined process does not necessarily need to be provided by a game system. For example, the predetermined process may be executed by a system different from the game system (the operator is also different).

[0145] Various elements included in an incomplete information game may be appropriately used as an element group. For example, in the case where an incomplete information game such as mahjong or a card game is provided, elements such as the hands of other users or the cards in hand (cards in one's possession) may be used as the element group. Also, like the mahjong tiles in mahjong, the element group for each user may be appropriately allocated and used from an element group (mahjong tiles) that is fixed and specified in common for all users. The same applies to card games such as seven-piece puzzles or poker. Alternatively, the incomplete information game may be played such that a partial element group arbitrarily designated by the user is used from the element group associated with each user. For example, in one aspect of the computer program of the present invention, the element group for each user may be formed by two or more elements selected by each user from the denominator element group associated with each user as the denominator of the element group.

[0146] The incomplete information game may be configured as appropriate. For example, in the incomplete information game, the available elements may be fixed, or may be variable according to appropriate conditions such as time. Also, changes in the available elements may occur as appropriate. For example, the number or type of available elements may change. That is, elements that become unavailable or newly added elements may occur according to a predetermined time or the like. When the available elements change, the change may or may not be reflected in the speculation information. Specifically, for example, in one aspect of the computer program of the present invention, the incomplete information game may be provided such that the available elements change at a predetermined time. Also, in this aspect, the change in the available elements may include at least one of a change in number and a change in type. Similarly, in the aspect where the available elements change, the predetermined process may be set to reflect the change in the available elements in the speculation information. When the available elements change according to time or the like, it is likely that it becomes more difficult to speculate on the elements to be concealed compared to the case where the available elements are fixed. Even when it is difficult to speculate on such concealed elements, the speculation can be assisted by the speculation information. And when the change in the available elements is reflected in the speculation information, the accuracy of the speculation information can be improved compared to the case where the same change is not reflected.

[0147] The incomplete information game may be configured as an appropriate game. For example, it may be configured as a cooperative game (including the case where it is played in a team), or a competitive game (including one-on-one, one-versus-many (such as in mahjong where a plurality of users other than oneself each become an independent opponent), and many-versus-many). Similarly, it may be configured as a game simply played by one person. Also, the incomplete information game may be a game in the real space or an electronic game. Therefore, the speculation information may be provided as appropriate. For example, in a game in the real space, the speculation information may be provided by being added to a video (such as display on a monitor or display on a wearable terminal) that captures the state of the game, or may be provided through sound in the real space where the game is played. Similarly, in an electronic game, the speculation information may be displayed on the game screen for the game or may be provided through sound. Also, the electronic game may be configured as appropriate, for example, it may be configured to be provided through an appropriate object of a display device that displays a game screen including various objects. Specifically, in one aspect of the computer program of the present invention, the incomplete information game may be provided as a card game in which the one user and the other user play against each other according to a predetermined rule through the plurality of card objects (CO) of a display device (MO) that displays a plurality of card objects that function as an element group of each user. Also, in this aspect, the display device may display a game screen (50) including the plurality of card objects, and the game system may provide the card game as a video game through the game screen.

[0148] Also, when a card game is provided as an incomplete information game, changes in available elements (for example, whether an element can be used due to a rule change, etc.) may or may not be reflected in the speculation information as described above. When reflected in the speculation information, the reflection may be realized as appropriate. For example, after a speculation result is obtained, the unavailability may be reflected in the speculation information simply by excluding elements that are determined to be unavailable from the speculation result. Alternatively, after elements that are determined to be unavailable are excluded, the speculation result may be adjusted again (such as reflecting the influence of the unavailability in other speculations) so that the unavailability is reflected in the speculation information. In any case, the unavailability may be reflected such that card objects that are determined to be unavailable are excluded from the speculation information. Specifically, for example, in an aspect where a card game is provided as an incomplete information game, changes in card objects that can be used in the card game include changes that make specific card objects unavailable due to a change in the rules, and the predetermined process may be set to reflect the unavailability of the specific card objects in the speculation information by excluding the specific card objects.

[0149] The card game may be played according to appropriate rules. For example, the card game may be played using fixed cards. Alternatively, the card game may be configured to grant appropriate card objects to users who meet predetermined granting conditions, and some of the card objects are owned by the users, and the game may be played using the card objects owned by each user. All of the card objects owned by each user may be used in the play, or only an appropriate part, such as the card objects selected by each user, may be used in the play. Specifically, for example, in the mode where a card game is provided as an incomplete information game, the card game is configured to grant a card object (CO) to a user who meets a predetermined granting condition, and the denominator element group may be composed of the card objects (CO) granted to each user. In this case, compared with the case where the available elements are fixed, the candidates for the elements to be concealed change for each play, so it is highly likely that the speculation becomes more difficult. And even when it is difficult to speculate on such concealed elements, the speculation can be assisted by the speculation information.

[0150] When a card game is provided as an incomplete information game, the card game may be configured to appropriately use a plurality of card objects (element groups). And an appropriate part of the plurality of card objects may be the target of the speculation information. For example, in the mode where a card game is provided as an incomplete information game, the card game is played using a plurality of card placement areas (CP) where the card objects to be used in the battle with the other users are to be arranged respectively, a hand area (57) where the hand cards (CO5) as candidate card objects to be arranged in each card placement area are to be arranged, and a deck area (55) where the deck (CO6) as candidate card objects to be added to the hand cards are to be arranged, and the predetermined process may be set so that the content of at least one card object among the plurality of card placement areas, the hand cards, and the deck of the other user can be obtained as the speculation information.

[0151] The predetermined process may be appropriately configured as long as inference information can be obtained. For example, each process may be configured to output inference information according to a predetermined logic (including predetermined rules, calculation formulas, etc.). Alternatively, the predetermined process may be configured as an artificial intelligence model (so-called AI) generated so as to obtain inference information. Further, the predetermined process may be configured as one type of process for obtaining one type of inference information, or may be configured as a plurality of processes for obtaining a plurality of inference information regarding one confidential element. For example, in one aspect of the computer program of the present invention, the predetermined process may be configured as a learned artificial intelligence model generated so as to obtain the inference information by machine learning a predetermined learning data set. Further, in this aspect, the learned artificial intelligence model may include a plurality of learned artificial intelligence models (24A, 24B) respectively generated by machine learning a plurality of learning data sets (FD) having different contents. In a card game, there may be a trend in the types (element groups) of card objects used. When there is a trend, an artificial intelligence model reflecting the trend is more likely to output accurate inference information. And trends often occur for each period. Similarly, there are often differences in the types and usage methods of card objects incorporated in the card object group used according to the rank (the skill of the user). For this reason, by learning a learning data set including the results for each predetermined period, the trend for each period can be reflected in the inference information. Alternatively, by learning a learning data set including the results for each rank, the usage tendency for each rank can be reflected in the inference information. By these, more accurate inference can be realized.

[0152] When the learned artificial intelligence model includes a plurality of learned artificial intelligence models, a plurality of pieces of speculation information obtained by the plurality of learned artificial intelligence models may be provided, or a part of the plurality of pieces of speculation information may be provided. When a part of the plurality of pieces of speculation information is provided, the part may be specified as appropriate. For example, it may be specified according to the play situation, or may be specified according to the evaluation (hit rate, user evaluation, etc.) over a certain period. Alternatively, it may be specified based on the designation by the user. For example, in an aspect where the learned artificial intelligence model includes a plurality of learned artificial intelligence models, the speculation information includes a plurality of pieces of speculation information respectively obtained by using the plurality of learned artificial intelligence models, and the information providing means may provide the speculation information corresponding to the learned artificial intelligence model selected by the one user among the plurality of pieces of speculation information to the one user.

[0153] On the other hand, the game system of the present invention is a game system in which a computer that provides element information as information regarding at least one of the element groups (CO) for each user in a game played by a plurality of users using the element groups for each user is incorporated. The computer is configured to use a predetermined process (24) set to obtain speculation information for speculating the content of the at least one element in an incomplete information game of a type in which at least one content of the element group of another user provided as the game is not shared with one user. An information acquisition means (35) for acquiring the speculation information, and an information providing means (35) for providing the speculation information as the element information.

[0154] In addition, the control method of the present invention is incorporated into a computer (31) incorporated in a game system (3) that provides element information as information regarding at least one of the element groups (CO) for each user in a game played by a plurality of users using the element groups for each user. In an incomplete information game of a type in which the content of at least one of the element groups of other users provided as the game is not shared with one user, a procedure for acquiring the speculation information using a predetermined process (24) set so as to obtain speculation information for speculating the content of the at least one element, and a procedure for providing the speculation information as the element information are executed.

[0155] (Reference Example) Non-Patent Document 1 discloses a technique of adding information such as "winning rate", "candidate moves", and "reading lines" simulated for each move by AI (artificial intelligence) to a relay screen obtained by photographing a shogi board during a game from above. However, this information (simulation result) is only a simulation result based on one type of AI.

[0156] Generally, AI tends to have a way of thinking (or perhaps it could be called personality) influenced by learning datasets and the like that form the basis of machine learning. For example, due to differences in the content of the learning dataset or the learning method (algorithm) of machine learning, etc., AI often forms its own unique way of thinking. Therefore, if the same move in the shogi game of Non-Patent Document 1 is simulated by multiple types of AI, there is a possibility that the simulation result of one AI may differ from that of another AI. When only the simulation result based on one type of AI is provided, the simulation result may be biased, or the simulation may be executed for a purpose different from the intention of the move. As a result, the reliability of the simulation result may decrease, or otherwise, a situation may arise where there is a wide demand for simulation results from AI with different ways of thinking, in other words, from various perspectives. The simulation result is not limited to AI and may also be calculated by various other logics, but the same situation can occur in that case as well.

[0157] Therefore, an object of the present invention is to provide a computer program or the like that can provide superiority-inferiority information from various perspectives for each option in a game including a selection opportunity to select a part of a plurality of options.

[0158] The computer program of the reference example functions as a superiority-inferiority acquisition means (35) that acquires a plurality of superiority-inferiority information for each option of at least one of the plurality of options by using a plurality of types of processes (23A, 23B) that output the superiority-inferiority information for each option of the at least one option to a computer (31) incorporated in a game system (3) that provides the superiority-inferiority information regarding the superiority or inferiority of the influence given by each option in a game including a selection opportunity to select a part of a plurality of options to a user before the selection of the part of the options, and a superiority-inferiority providing means (35) that provides the plurality of superiority-inferiority information for each option before the selection of the part of the options.

[0159] According to the reference example, a plurality of superiority / inferiority information is output for each option by a plurality of types of processing, and they are provided as superiority / inferiority information before the selection of some options. Therefore, in a game including a selection opportunity for selecting a part of a plurality of options, superiority / inferiority information can be provided from various viewpoints for each option. Note that the game of the reference example may be a game in the real space and does not necessarily have to be provided by a game system.

[0160] Superiority / inferiority information may be provided in an appropriate game including a selection opportunity. For example, in a cooperative game including a selection opportunity (including the case of playing in a team), or a competitive game (including one-on-one, one-on-many (including the case where a plurality of users other than oneself are independent opponents in a game such as mahjong), and many-on-many), superiority / inferiority information may be provided. Similarly, superiority / inferiority information may be provided in a game played simply by one person. Also, in each game, various influences given by each option may be output as superiority / inferiority information. For example, in a game having a concept of victory, the influence on victory may be output as superiority / inferiority information. Alternatively, the influence on a part of the progress, such as a specific development (including a specific mission and the acquisition of bonus points, etc.) provided in the game, may be output as superiority / inferiority information. Specifically, for example, in one aspect of the computer program of the reference example, as the game, a competitive game in which the user plays against an opponent is provided, and the plurality of types of processing may be configured to output any information regarding the superiority / inferiority of the influence that each option gives to victory in the competitive game as the plurality of superiority / inferiority information. In this case, information regarding the influence on victory in the competitive game can be provided from various viewpoints for each option.

[0161] The superiority / inferiority information may be appropriate information regarding superiority and inferiority. For example, it may be information on symbols that distinguish the degrees of superiority and inferiority such as A, B, and C, or it may be information that sensuously expresses superiority and inferiority such as "seems good" and "seems bad". Alternatively, the superiority / inferiority information may be information in which superiority and inferiority are quantified. When superiority and inferiority are quantified, the numerical value may be a numerical value that indicates the possibility of progressing advantageously (being less likely to become disadvantageous) within a predetermined range (including an appropriate range such as a predetermined number of selection opportunities, a predetermined time, or a predetermined development), or of course, it may be a numerical value that indicates the possibility of ultimately winning in a confrontation. For example, in an aspect where a confrontation-type game is provided, the plurality of types of processing may be configured to output, as the plurality of superiority / inferiority information, any numerical information that indicates, through the magnitude of the numerical value, the possibility of becoming advantageous in the confrontation-type game.

[0162] The confrontation-type game may be appropriately configured. For example, the confrontation-type game may be configured such that the user and the opponent execute selections at any time (in parallel), or it may be configured to be given turns that include alternating selection opportunities. Even when turns are given alternately, for example, a selection by a person outside the turn (this selection may function as a selection opportunity in a reference example) may not be completely excluded, such as when a selection by the opponent is required following the user's selection in the user's turn. Also, when turns are given alternately, the alternation of turns may be automatically executed according to time, the number of instructions, etc., or it may be executed as one of the selections in each turn. When the turn alternates by selection, the selection may function as an object of superiority / inferiority information or may be excluded from the object of superiority / inferiority information. Also, each turn may be appropriately configured. For example, it may include only one selection opportunity at a time, or it may include a plurality of selection opportunities. The plurality of selection opportunities may be appropriately distinguished through phases, etc., and the options in each selection opportunity may all be the same, or at least some (including all) of the options may be different. Also, the end of each phase may be automatic or selective. When the end of each phase is selective, the selection may or may not be an object of superiority / inferiority information.

[0163] For example, in an aspect where a competitive game is provided, the competitive game is configured such that turns including the selection opportunity are provided alternately to the user and the opponent, and the plurality of options may include an option corresponding to a turn end instruction for ending the user's turn. Also, in this aspect, each turn includes a plurality of phases that each provide a plurality of selection opportunities, and the selection opportunities for each phase are configured to each provide, as the plurality of options, two or more options including different options among the selection opportunities for each phase, and the two or more options for each selection opportunity may each include an option corresponding to a phase end instruction for ending each phase. When the alternation of turns functions as one of the options, the influence of the selection in the selection opportunity on the progress tends to become more complicated. When a plurality of selection opportunities are included in each turn and each of those selection opportunities ends selectively, it is more likely to become even more complicated. When an option corresponding to a turn end instruction or the like is the subject of superiority / inferiority information, a plurality of superiority / inferiority information can be provided from various viewpoints regarding the complicated selection.

[0164] A game including a selection opportunity may be a game in the real space or an electronic game. For this reason, the superiority / inferiority information may be provided as appropriate. For example, in a game in the real space, the superiority / inferiority information may be provided by being added to a video (display on a monitor, display on a wearable terminal, etc.) that captures the state of the game, or may be provided through sound in the real space where the game is played. Similarly, in an electronic game, the superiority / inferiority information may be displayed on the game screen for the game, or may be provided through sound. Also, the electronic game may be configured as appropriate, for example, may be configured to be provided via an appropriate object of a display device that displays a game screen including various objects. Specifically, in an aspect where a competitive game is provided, the competitive game may be provided as a game that competes with an opponent according to a predetermined rule via the plurality of objects of a display device (MO) that displays a plurality of objects (CO).

[0165] When a competitive game is provided via a plurality of objects, the plurality of objects may be used as appropriate. For example, when an electronic shogi or a chess game is provided, electronic pieces may be used as the plurality of objects. Alternatively, when an electronic mahjong or a card game is provided, electronic mahjong tiles or cards may be used as the plurality of objects. Also, in a game including various characters (including not only human-like entities but also various objects such as vehicles and animals), the plurality of characters may function as the plurality of objects. These objects may be used as appropriate according to the rules of the game. Similarly, various elements set according to the rules of the game may function as a plurality of options. For example, each object (e.g., a character) may or may not function as an option.

[0166] Specifically, for example, in an aspect where a competitive game is provided, the competitive game is provided as a card game that uses a plurality of card objects (CO) as the plurality of objects, and the card game includes a plurality of card placement areas (CP) where the card objects to be used in the game against the opponent are to be placed respectively, a hand area (57) where a hand (CO5) as a candidate card object to be placed in each card placement area is to be placed, and a deck area (55) where a deck (CO6) as a candidate card object to be added to the hand is to be placed, and is played using these, and the plurality of options may include options corresponding to at least one of the plurality of card placement areas, the hand to be placed in each card placement area, and the addition of the deck to the hand. In this case, a plurality of superiority and inferiority information can be provided from various viewpoints in a game that requires complex selections including many options (including options further derived from each option).

[0167] The plurality of types of processing may be configured as appropriate as long as they can output a plurality of superiority / inferiority information. For example, each processing may be configured to output superiority / inferiority information according to a predetermined logic (including a predetermined rule, calculation formula, etc.). Alternatively, at least one of the plurality of types of processing may be configured as an artificial intelligence model (so-called AI) generated to output superiority / inferiority information. Specifically, in one aspect of the computer program of the reference example, at least one of the plurality of types of processing is configured as a learned artificial intelligence model generated by causing a predetermined pre-learning model to perform machine learning on a predetermined learning dataset so as to output information on superiority / inferiority regarding a predetermined result in the game as the superiority / inferiority information. Also, in this aspect, the plurality of types of processing may all be configured as the learned artificial intelligence model, and may be configured to output the plurality of superiority / inferiority information due to at least one difference among the predetermined result, the learning dataset, and the algorithm of the pre-learning model for learning the learning dataset.

[0168] The plurality of superiority / inferiority information may be provided as appropriate. For example, the superiority / inferiority information may be provided according to an order set in advance for each option, or when digitized, may be provided in an order according to the magnitude of the numerical values, such as in descending order. Also, the order of providing each superiority / inferiority information may be fixed or variable. For example, the plurality of superiority / inferiority information may be provided in the order specified by the user. Alternatively, the plurality of superiority / inferiority information may be provided with some being limited according to a predetermined filter condition. The filter condition may be fixed or variable. For example, the filter condition may be specified by the user. Specifically, in one aspect of the computer program of the reference example, the superiority / inferiority providing means may be provided with at least one of a filter means (35) for limiting a part of the plurality of superiority / inferiority information to satisfy a predetermined filter condition and a sorting means (35) for sorting the plurality of superiority / inferiority information according to a predetermined sorting condition. In this case, the plurality of superiority / inferiority information is provided with some being limited or sorted as appropriate through the filter condition or the like. Therefore, the convenience of accessing specific superiority / inferiority information through the filter condition or the like can be improved.

[0169] On the other hand, a game system of a reference example is a game system (3) incorporating a computer (31) that provides a user with superiority-inferiority information regarding the superiority or inferiority of the influence exerted by each option in a game including an opportunity to select a part of a plurality of options, before the selection of the part of the options. The computer functions as a superiority-inferiority acquisition means (35) that acquires a plurality of pieces of superiority-inferiority information for each option of the at least one option by using a plurality of types of processes that output the superiority-inferiority information for each of the at least one option, and a superiority-inferiority providing means (35) that provides the plurality of pieces of superiority-inferiority information for each option before the selection of the part of the options.

[0170] In addition, a control method of a reference example causes a computer (31) incorporated in a game system (3) that provides a user with superiority-inferiority information regarding the superiority or inferiority of the influence exerted by each option in a game including an opportunity to select a part of a plurality of options, before the selection of the part of the options, to execute a procedure of acquiring a plurality of pieces of superiority-inferiority information for each option of the at least one option by using a plurality of types of processes that output the superiority-inferiority information for each of the at least one option, and a procedure of providing the plurality of pieces of superiority-inferiority information for each option before the selection of the part of the options.

Description of Reference Numerals

[0171] 3 User device (game system) 31 Control unit (computer) 35 Model processing unit (superiority-inferiority acquisition means, superiority-inferiority providing means, filter means, sorting means, information acquisition means, information providing means) 50 Battle screen (game screen) 55 Main deck placement area (deck area) 57 Hand placement area (hand area) CO Card object (object) CP Card placement area (card placement area) MO Display device CO5 Hand CO6 Deck PG2 Game Program (Computer Program)

Claims

1. A computer incorporated into a game system that provides element information as information regarding at least one of the element groups for each user in a game played by a plurality of users using the element groups for each user, in an incomplete information game of a type in which at least one content of the element groups of other users provided as the game is not shared with one user, information acquisition means for acquiring the speculation information by using a predetermined process set so as to obtain speculation information for speculating the content of the at least one element, and information providing means for providing the speculation information as the element information, A computer program configured to function as.

2. The element group of each user is formed by two or more elements selected by each user from a denominator element group associated with each user as a denominator of the element group, The computer program according to claim 1.

3. The incomplete information game is provided such that available elements change at a predetermined time, The computer program according to claim 2.

4. The change in the available elements includes at least one of a change in number and a change in type, The computer program according to claim 3.

5. The predetermined process is set to reflect the change in the available elements in the speculation information, The computer program according to claim 3.

6. The incomplete information game is provided as a card game in which one user and other users play against each other according to a predetermined rule via the plurality of card objects of a display device that displays a plurality of card objects functioning as the element group of each user, The computer program according to claim 5.

7. The change in the card objects available in the card game includes a change that makes a specific card object unusable with the change of the rule, The predetermined process is set to reflect the unusability of the specific card object in the speculation information by excluding the specific card object, The computer program according to claim 6.

8. The card game is configured to give a card object to a user who satisfies a predetermined giving condition, The computer program according to claim 6, wherein the denominator element group is composed of card objects assigned to each user.

9. The card game is played using a plurality of card placement areas where card objects to be used in a battle with the other user are to be placed respectively, a hand area where a hand of card objects to be candidates to be placed in each card placement area is to be placed, and a deck area where a deck of card objects to be candidates to be added to the hand is to be placed. The computer program according to claim 8, wherein the predetermined process is set so that the content of at least one card object among the plurality of card objects of the other user in the plurality of card placement areas, the hand, and the deck is obtained as the speculation information.

10. The display device displays a game screen including the plurality of card objects. The computer program according to any one of claims 6 to 9, wherein the game system provides the card game as a video game via the game screen.

11. The computer program according to any one of claims 1 to 9, wherein the predetermined process is configured as a learned artificial intelligence model generated so that the speculation information is obtained by machine learning a predetermined learning dataset.

12. The computer program according to claim 11, wherein the learned artificial intelligence model includes a plurality of learned artificial intelligence models respectively generated by machine learning a plurality of learning datasets having different contents.

13. The speculation information includes a plurality of speculation information respectively obtained using the plurality of learned artificial intelligence models. The computer program according to claim 12, wherein the information providing means provides the speculation information corresponding to the learned artificial intelligence model selected by the one user among the plurality of speculation information to the one user.

14. A game system incorporating a computer that provides element information as information regarding at least one of the element groups for each user in a game played by a plurality of users using the element groups for each user. The computer is In an incomplete information game of a type in which at least one content of an element group of another user provided as the game is not shared with a single user, information acquisition means for acquiring the inference information by using a predetermined process set so as to obtain inference information for inferring the content of the at least one element, and information providing means for providing the inference information as the element information, A game system that functions as [

15. ] In a computer incorporated in a game system that provides element information as information regarding at least one of the element groups for each user in a game played by a plurality of users using the element groups for each user, In an incomplete information game of a type in which at least one content of an element group of another user provided as the game is not shared with a single user, a procedure for acquiring the inference information by using a predetermined process set so as to obtain inference information for inferring the content of the at least one element, and a procedure for providing the inference information as the element information, A control method for causing the above to be executed.

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