Computer program, game system using 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, etc., that is applied to a computer incorporated into a game system that provides element information as information regarding at least one of a group of elements for each user in a game played by multiple users using a group of elements for each user. [Background technology]

[0002] There are game systems that provide element information as information on at least one of the element groups for each user in a game played by multiple users using element groups for each user. For example, a system is known that employs information on a shogi game in real space, each move in the shogi game, and the "win rate" for each move as the game, element group, and element information, respectively (see, for example, Non-Patent Document 1). [Prior art documents] [Non-patent literature]

[0003] [Non-Patent Document 1] Ryosuke Mine, "The win rate display is interesting! How to enjoy the 'ABEMA Shogi Program' where AI analyzes the win rate," [online], December 26, 2020, Shogakukan, [Retrieved January 12, 2024], Internet,<URL:https: / / dime.jp / genre / 1032083 / > Summary of the Invention [Problem to be solved by the invention]

[0004] Non-Patent Document 1 discloses a technology that adds information such as "winning rate," "potential moves," and "reading lines" simulated by AI (artificial intelligence) for each move to a live screen that captures a shot of the shogi board from above during a game. Games like shogi are known as complete information games, in which all information about the pieces used by each player is disclosed. On the other hand, there are also games known as imperfect information games. In these types of games, the content of at least one element of a group of elements (e.g., options) used by other users (including computers) is not shared with a single user. For this reason, in imperfect information games, information about elements that are not shared (concealed) with users can be important.

[0005] Therefore, an object of the present invention is to provide a computer program or the like that can help infer hidden elements in an incomplete information game. [Means for solving the problem]

[0006] The computer program of the present invention is configured to cause a computer incorporated in a game system that provides element information as information relating to at least one of a group of elements for each user in a game played by multiple users using each group of elements for each user to function as an information acquisition means that acquires the guessed information using a predetermined process set up to obtain guessed information for guessing the content of at least one element in an incomplete information game provided as the game, in which the content of at least one of the group of elements of other users is not shared with a single user, and an information provision means that provides the guessed information as the element information.

[0007] On the other hand, the game system of the present invention is a game system incorporating a computer that provides element information as information relating to at least one of the element groups for each user in a game played by multiple users using element groups for each user, and the computer functions as an information acquisition means that acquires the guessed information using a predetermined process set up to obtain guessed information for guessing the content of at least one element in an incomplete information game provided as the game, in which the content of at least one of the element groups of other users is not shared with a single user, and an information provision means that provides the guessed information as the element information.

[0008] Furthermore, the control method of the present invention causes a computer incorporated in a game system that provides element information as information relating to at least one of the element groups for each user in a game played by multiple users using element groups for each user to execute the following steps 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 a single user: acquiring the guessed information using a predetermined process set up to obtain guessed information for guessing the content of the at least one element; and providing the guessed information as the element information. [Brief explanation of the drawings]

[0009] [Figure 1] 1 is a diagram showing a schematic configuration of a network system to which a game system according to an embodiment of the present invention is applied; [Figure 2] FIG. 2 is a functional block diagram showing the main parts of a control system of the network system. [Figure 3] FIG. 10 is a diagram schematically illustrating an example of a battle screen for playing a card game. [Figure 4] FIG. 10 is a diagram showing an example of a procedure for playing a card game. [Figure 5] FIG. 10 is a diagram schematically illustrating an example of a battle screen when an analysis service is applied. [Figure 6]FIG. 10 is an explanatory diagram illustrating a list of options (actions) that a player can take in a certain situation on the battle screen. [Figure 7] FIG. 10 is an explanatory diagram illustrating an example of a method in which an artificial intelligence model calculates a probability value. [Figure 8] FIG. 10 is a diagram schematically illustrating an example of a battle screen when a guessing service is applied. [Figure 9] FIG. 10 is an explanatory diagram illustrating an example of a method in which an artificial intelligence model calculates inferred information. [Figure 10] FIG. 10 is an explanatory diagram for explaining an example of the types of inferred information output by a plurality of types of artificial intelligence models. [Figure 11] FIG. 10 is an explanatory diagram for explaining a learning dataset for realizing estimated information for each period and each rank. [Figure 12] 10 is a flowchart showing an example of a procedure for option analysis processing. [Figure 13] 10 is a flowchart showing an example of a procedure for a display change process. [Figure 14] 10 is a flowchart showing an example of a procedure for a back-side inference process. [Figure 15] FIG. 10 is an explanatory diagram illustrating an example of a modified example regarding the display mode of superiority / inferiority information. DETAILED DESCRIPTION OF THE INVENTION

[0010] (Overall composition) A control method according to an embodiment of the present invention, and a game system in which a computer program is implemented (a game system according to an embodiment of the present invention) will be described below with reference to the drawings. First, 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 with reference to Fig. 1. As shown in Fig. 1, the network system 1 is configured as a client-server 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 used daily by a user and is a computer device (information communication terminal device) equipped with an information communication function via the network NT. The user device 3 is equipped with a computer program according to an embodiment of the present invention and functions as a game system according to an embodiment of the present invention in the network system 1. As an example, a smartphone or tablet terminal equipped with a communication function may be used as the user device 3. The user device 3 may be a PC (short for personal computer) or a personal or home game machine provided as a so-called consumer game machine. Furthermore, an arcade game machine may also be used as the user device 3.

[0012] The user device 3 functions as a game machine by implementing predetermined software (applications) and provides games. The user device 3 provides games including selection opportunities in which a user must select one of multiple options. Such games may be configured as appropriate games such as role-playing games, simulation games, or action games. As an example, the game may be configured as a competitive video game in which the user of the user device 3 competes against opponents (other users, including computers) according to predetermined rules via a game screen including multiple objects. In a competitive game, the user and the opponents may function as members of a team, and the competitive game may be played in a one-on-many format (including cases in which multiple users other than the user are independent opponents, such as in mahjong) or a many-on-many format. However, the following describes a one-on-one format as an example.

[0013] Furthermore, the competitive game may be provided as a perfect information game such as shogi or chess (both of which use pieces as objects), but as an example, it is provided as an imperfect information game. In contrast to a perfect information game in which all of the contents of the various elements necessary for selection in a selection opportunity are shared with the user, an imperfect information game is a type of game in which the contents of at least one element of a group of elements (such as the contents of cards in a card game) used by other users (including computers) are not shared with the user. Imperfect information games include various games such as mahjong, but the following describes the case in which a card game is provided as an example.

[0014] The game server 2 may be configured by appropriately combining multiple 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. These services include an analysis service and a guessing service. Both the analysis service and the guessing service are services for assisting selection in selection opportunities in the game. Specifically, the analysis service is configured to provide the user with analysis results of superiority / inferiority information regarding the influence of each option included in the selection opportunity for at least one option before selection. On the other hand, the guessing service is configured to provide guess information regarding information that is not shared with the user in the card game. The analysis service and the guessing service will be described in detail below.

[0015] In addition, the game server 2 may also provide the user device 3 with other services, such as a distribution service that distributes computer programs and various data necessary for playing games on the user device 3, a matching service that matches users who cooperate or compete in games, and a relay service that relays game information that should be shared between user devices 3.

[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 Figure 1, the game server 2 is connected to the network NT via a router NTr, and the user device 3 is connected to the network NT via an access point PP.

[0017] (Network system control system) Next, the main parts of the control system of the network system 1 will be described with reference to Figure 2. First, the game server 2 is provided with a control unit 21 and a storage unit 22 as storage means. The control unit 21 is configured as a computer that combines a processor unit that executes various arithmetic processes and operational control according to a predetermined computer program with an internal memory and other peripheral devices required for the operation. The processor unit may include, as appropriate, 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 appropriately integrated, such as when a GPU is incorporated into a CPU).

[0018] The memory unit 22 is an external storage device realized by a memory unit including a non-volatile memory medium (computer-readable memory medium) such as a hard disk array. The memory unit 22 may be configured to store all data on one memory unit, or may be configured to store data in a distributed manner across multiple memory units. The memory unit 22 stores a server program PG1 and server data SD. The server program PG1 is a computer program that causes the control unit 21 to execute processes required to provide various services to the user device 3. The server program PG1 may include various programs as appropriate depending on the processes to be executed by the control unit 21, and in the example of FIG. 2, it includes an analysis program AP and an inference 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 configured as appropriate, for example, to cause the control unit 21 to execute a process for outputting analysis results based on predetermined logic (including predetermined rules such as rules), but in the example of FIG. 2, it is configured to cause the control unit 21 (e.g., a GPU) to function as an artificial intelligence model that outputs analysis results. The analysis program AP may be configured as appropriate, but in the example of FIG. 2, it is configured as an inference program incorporating analytical trained data (parameters) AD that are output by having a training program (for analysis) learn a training dataset (for analysis).

[0020] The analytical trained data AD may be of a single type, but the example of Figure 2 includes multiple types of trained data AD. The multiple types of analytical trained data AD may be generated by applying the same training data set to training programs with different algorithms (learning methods), but as an example, they are generated by applying different training data sets to the same training program. The analysis program AP incorporates and executes each of the multiple types of analytical trained data AD, causing the control unit 21 to function as an artificial intelligence model with different ways of thinking. In the example of Figure 2, the analysis programs AP incorporating each of the multiple types of analytical trained data AD are distinguished as a first analysis program AP1, a second analysis program AP2, and so on. For this reason, the analysis program AP is provided with multiple analysis programs AP, such as the first analysis program AP1 and the 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 the inference service. The inference program FP may be configured as appropriate, for example, to cause the control unit 21 to execute a process for outputting an inference result based on predetermined logic, but in the example of Fig. 2, it is configured to cause the control unit 21 (e.g., a GPU) to function as an artificial intelligence model, similar to the analysis program AP. The inference program FP may also be configured as appropriate, but in the example of Fig. 2, it is configured as an inference program incorporating inference-use trained data (parameters) FD that are output by having a learning program (for inference) learn a learning dataset (for inference).

[0022] Although the trained data for inference FD may be of a single type, the example of Figure 2 includes multiple types of trained data for inference FD (which do not have to match the number of trained data for analysis AD), similar to the trained data for analysis AD. Multiple types of trained data for inference FD may be generated by applying the same training data set to training programs with different algorithms (learning methods), but as an example, they are generated by applying different training data sets to the same training program. The inference program FP incorporates and executes each of the multiple types of trained data for inference FD, causing the control unit 21 to function as an artificial intelligence model with different ways of thinking. In the example of Figure 2, the inference programs FP, each incorporating multiple types of trained data for inference FD, are distinguished as a first inference program FP1, a second inference program FP2, and so on. For this reason, multiple inference programs FP, such as the first inference program FP1 and the second inference program FP2, are provided.

[0023] The server data SD is data referenced by the server program PG1 to provide various services. The server data may include appropriate data related to the various services. The example in FIG. 2 shows play data PD, analytical learning data AD, and estimation learning data FD as examples. The play data PD describes information about each user's past playing history. The play data PD may include information necessary for managing each user, such as personal information including attributes such as the user's gender or address, in addition to the playing history. Each user's possessions in the card game may be managed as appropriate, but are managed by the play data PD, for example. Specifically, the card game is configured to grant card objects to users who meet certain granting conditions (such as purchase, loan, lottery, or granting due to game progress). The card objects granted to each user (hereinafter sometimes referred to as "owned cards") are managed in association with each user in the play data PD.

[0024] The control unit 21 can be provided with various logical devices by combining the control unit 21's hardware resources and the server program PG1 as a software resource, but in the example of Fig. 2, an analytical model unit 23 and an inference model unit 24 are provided. Both the analytical model unit 23 and the inference model unit 24 are logical devices that function as artificial intelligence models. Specifically, the analytical model unit 23 functions as an artificial intelligence model that outputs analysis results based on the analytical program AP, and the inference model unit 24 functions as an artificial intelligence model that outputs inference results based on the inference program FP.

[0025] The analytical model unit 23 (artificial intelligence model) is realized, for example, by a combination of analytical trained data AD and an analytical program AP (the analytical program AP executed by incorporating the analytical trained data AD), and performs various processes to realize analytical services. This type of artificial intelligence model can form different thoughts depending on various factors. For example, it tends to have different thoughts depending on at least one difference in the content of the training dataset, the learning program algorithm, and the inference program algorithm. The analytical model unit 23 can be provided with an appropriate type of analytical model unit 23 depending on these differences. The example in Figure 2 shows two types of analytical model units 23: a first analytical model unit 23A and a second analytical model unit 23B.

[0026] The first analytical model unit 23A is the analytical model unit 23 corresponding to the first analytical program AP1. Similarly, the second analytical model unit 23B is the analytical model unit 23 corresponding to the second analytical program AP2. The first analytical model unit 23A and the second analytical model unit 23B execute various processes in accordance with the first analytical program AP1 or the second analytical program AP2. For example, these processes include option analysis processing. The procedure for option analysis processing will be described in detail later.

[0027] Similarly, the inference model unit 24 (artificial intelligence model) is realized, for example, by a combination of learned data for inference FD and an inference program FP (the inference program FP executed by incorporating the learned data for inference FD), and performs various processes to realize the inference service. The inference model unit 24 may also be provided with an appropriate type (including one type) of inference model unit 24. The example in Figure 2 shows two types of inference model unit 24: a first inference model unit 24A and a second inference model unit 24B.

[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 processes include a back-side estimation process. The procedure of the back-side estimation process will be described in detail later.

[0029] In addition, the control unit 21 may be provided with a logical device such as a Web service management unit for implementing processes related to various services, such as the distribution service, matching service, or relay service described above. Similarly, input devices such as a keyboard and output devices such as a monitor may be connected to the control unit 21 as needed. However, these are not shown in the drawings.

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

[0031] The memory unit 32 is an external storage device realized by a storage unit including a non-volatile storage medium (computer-readable storage medium) such as a hard disk or semiconductor storage device. The memory unit 32 stores a game program PG2 and game data GD. The game program PG2 is a computer program that causes the control unit 21 to execute processes required to make the user device 3 function as a game device. The game data GD is data referenced by the game program PG2 to provide the game. The user device 3 may be provided with an analysis program AP or an inference program FP from the game server 2 as appropriate, and in that case, the game data GD may include the analysis program AP or the inference program FP.

[0032] The game data GD may include various data (including various tables) necessary for playing the game, such as image data for displaying various images for the game, audio data for playing various sounds (including background music such as music), and card data defining each card object, as appropriate, but the example in Figure 2 shows play data PD. The play data PD is provided and saved from the game server 2 as needed. Furthermore, when the analysis program AP or the estimation program FP is provided to the user device 3 from the game server 2, the game data GD may include analysis learned data AD and estimation learned data FD.

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

[0034] The progress control unit 33 executes various processes necessary for the progress of the card game. These processes include processes necessary for enjoying the services provided by the game server 2. For example, the progress control unit 33 executes processes related to opponent matching in cooperation with the Web service management unit, and processes for reflecting the opponent's play on the player's progress during the match, and vice versa. The progress control unit 33 also executes processes for providing selection opportunities in each turn and phase, which will be described later, and for switching between turns and phases.

[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 analysis services and inference services to users of the user devices 3. The processes executed by the model processing unit 35 include processes realized in cooperation with the analytical model unit 23 or the inference model unit 24 of the game server 2. For example, the model processing unit 35 executes processes in cooperation with the analytical model unit 23 to analyze the impact that at least one of multiple options selectable in a selection opportunity has on the progress of the card game, and to provide the analysis results. Similarly, the model processing unit 35 executes processes in cooperation with the inference model unit 24 to provide inference information regarding information that is not shared with users in the card game.

[0037] For example, the model processing unit 35 executes a choice analysis process in cooperation with the analytical model unit 23 of the game server 2 as a process for realizing the analysis service. The model processing unit 35 also executes a display change process. Similarly, the model processing unit 35 executes a behind-the-scenes inference process in cooperation with the inference model unit 24 of the game server 2 as a process for realizing the inference service. The procedure of the display change process will be described in detail later.

[0038] The model processing unit 35 also generates a situation log SL necessary for analysis by the analytical model unit 23 or for inference by the inference model unit 24, stores the situation log SL in the internal storage device of the control unit 31, and executes processing to appropriately update the situation log SL as the situation changes. The situation log SL may appropriately include various information related to the situation of the game. The situation log SL (data) includes various information (which may be limited to an appropriate portion of the information) for determining the situation of the card game, such as information on all card objects currently in use, information on each card object placed in various placement locations described below, attributes of each card object (including the status of each card object, such as whether it has been summoned or left the field area, described below), life points, phases, and information on options selectable in each situation.

[0039] The user device 3 is provided with appropriate output devices and input devices, and the example in FIG. 2 shows a monitor MO, a speaker SK, and a touch sensor TS as examples of output devices, etc. All of these are general-purpose hardware provided in information communication terminals such as smartphones. For example, the touch sensor TS is an input device that inputs signals corresponding to a user's touch operation (operation of touching with a finger) to the control unit 31. The speaker SK is an output device that reproduces various sounds in response to signals from the control unit 31. The monitor MO is an output device (display device) that presents a game screen, etc. in response to signals from the control unit 31. Note that the user device 3 may also be provided with various other devices, such as a gyro sensor, an acceleration sensor, a location information (e.g., GPS information) receiving device, etc., as appropriate.

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

[0041] FIG. 3 is a diagram schematically illustrating 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 battle using their respective deck cards. Various card objects may be arranged (displayed) on the battle screen 50. However, in the example of FIG. 3, for ease of explanation, regardless of type or content, card objects CO facing up (the direction in which the contents of the card object are visible) are shown with a dotted pattern, and card objects facing down (the direction in which the contents of the card object are not visible and are concealed) are shown with a diagonal right-hand line. In this example, a predetermined number of card objects CO (which may be constant or variable, and may not match the number of deck cards in the opponent's deck) included in the user's deck card function as the plurality of objects and card objects of the present invention. Furthermore, the owned card group and the deck cards function as the denominator element group and two or more elements of the present invention, respectively.

[0042] As shown in Fig. 3, the battle screen 50 includes a user area 51, a common 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, but 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 multiple card places CP (only some of the symbols are shown) are formed. Each card place CP is a location (area) where each card object CO of the deck cards is placed to progress the game. A card game may be provided with various card objects CO with various roles (purposes) and effects. For example, card objects CO may be provided such as a card object CO representing a monster (hereinafter sometimes referred to as a "monster card CO1"), a card object CO representing a magic spell (a predetermined effect) (hereinafter sometimes referred to as a "magic card CO2"), a card object CO representing a trap (a type of effect different from magic) (hereinafter sometimes referred to as a "trap card CO3"), or a card object CO representing a special effect (hereinafter sometimes referred to as a "special card CO4"). When a card object CO is placed in each card place CP, the card object CO actually performs its role in the battle.

[0044] For example, a monster card CO1 is assigned a role such as attacking or defending the opponent or the opponent's monster cards CO1. Monster cards CO1 can be classified into various categories, including two types: a normal monster card CO1 and an extra monster card CO1. A normal monster card CO1 is a monster card CO1 that can be placed from the hand placement area 57 to the field area 54. An extra monster card CO1 is a monster card CO1 that is summoned (called) in exchange for a monster card CO1 leaving the field area 54. The placement (summoning) of an extra monster card CO1 is usually restricted to the common area 52, but placement in the field area 54 may be permitted if certain field conditions are met. For this reason, a monster card CO1 may have the role of summoning (calling) other monster cards CO1 to the field area 54, etc. Monster cards CO1 placed in the card placement area CP are used to actually assign roles such as attack, defense, or summon to the game's progress. Similarly, magic card CO2, trap card CO3, and special card CO4 are card objects CO that can be placed from the hand placement area 57 to the field area 54, and magic card CO2, trap card CO3, and special card CO4 placed in the card placement area CP are used to activate the effects indicated by each card object CO.

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

[0046] Specifically, the front row 54A is used as card placement spaces CP where monster cards CO1, one of the card object CO types, should be placed. However, of the six card placement spaces CP that make up the front row 54A, the leftmost card placement space CP (which may be formed as desired, but in the example of FIG. 3, it is formed slightly smaller than the other card placement spaces CP) is the only exception, and is used as a card placement space CP where special cards should be placed. In other words, placement of card objects CO such as magic cards CO2 or trap cards CO3 in the front row 54A is restricted, placement of special cards CO4 in any card placement space other than the leftmost card placement space CP in the front row 54A is restricted, and placement of monster cards CO1 in the leftmost card placement space CP is further restricted.

[0047] On the other hand, the back row 54B is used as card placement areas CP for magic cards CO2 and trap cards CO3, among the card object CO types. In other words, placement of card objects CO such as monster cards CO1 or special cards CO4 in the back row 54B is restricted. Note that monster cards CO1 with the characteristics of magic cards CO2 (monster cards CO1 corresponding to monsters with magical effects) may be prepared, and such monster cards CO1 with magical attributes may be permitted to be placed in predetermined card placement areas CP in the back row 54B (for example, card placement areas CP at both the left and right ends). In other words, card objects CO with multiple attributes may be included, and placement of such card objects CO may be permitted in both the front row 54A and the back row 54B depending on the multiple attributes.

[0048] In the field area 54, card objects CO can be placed as appropriate according to the user's instructions, under the restrictions of each card placement area CP, and in the example of Fig. 3, one monster card CO1 is placed in the third card placement area CP from the right in the front row 54A. Furthermore, each card object CO may be placed in various directions in the card placement area CP, such as vertically or horizontally, and although the placement direction, such as vertically, can be appropriately selected, as an example, the monster card CO1 is placed horizontally when used for defense and vertically when used for attack.

[0049] The hand placement area 57 is an area where card objects CO (hereinafter, sometimes referred to as hand CO5) from the deck cards that are virtually at hand should be placed (displayed). The hand CO5 is a candidate card object CO to be placed in the field area 54 (each card placement area CP). Any number of hand cards CO5 can be placed in the hand placement area 57, and in the example of FIG. 3, five hand cards CO5 are placed. In this example, the hand placement 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 representing the remaining deck cards (the original deck cards minus the card objects CO in the field area 54 and the hand placement area 57) should be placed, but they have different uses. Specifically, the main deck placement area 55 is an area where card objects CO (hereinafter sometimes referred to as the deck CO6) that are candidates for addition to the hand CO5 should be placed from the remaining deck cards. The card objects CO in the deck CO6 are added to the hand CO5 through a draw in the draw phase, which will be 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 summon deck cards CO7) representing a stack of extra monster cards CO1 should be placed. When an extra monster card CO1 is summoned via a normal monster card CO1, the extra monster card CO1 to be placed in the field area 54 is drawn from the summon deck cards CO7. That is, the remaining deck cards are divided into stock cards CO6 and summon deck cards CO7, and are placed respectively in the main deck placement area 55 and the extra deck placement area 56. In this example, the main deck placement area 55 functions as the stock card area of ​​the present invention.

[0051] In principle, the graveyard area 58 is an area for storing card objects CO that have left the field area 54. For example, a monster card CO1 will leave the field area 54 when a predetermined condition (a condition for leaving the field area 54, etc.) is met, such as an attack by the opponent's monster card CO1 or the summoning of an extra monster card CO1. Similarly, card objects CO that exert predetermined effects, such as a magic card CO2 or a trap card CO3, will leave the field area 54 according to the card's effect after activating that effect. The graveyard area 58 is provided as a place (destination) for storing (placing) card objects CO that have left the field area 54. The graveyard area 58 may be configured to display card objects CO that have left the field area 54, but in the example of FIG. 3, the display of card objects CO is omitted.

[0052] The point display area LR is an area for displaying the user's life points. In a card game, the user and the opponent may each set their own life points, but in the example of FIG. 3, both sets of life points are set to 8000 points (“8000”). The win / loss condition for determining victory or defeat in a battle may be set as appropriate, but is met, for example, when the opponent's life points are reduced to zero. In other words, life points function as a parameter for determining victory or defeat. Specifically, the user can reduce the opponent's life points by using monster cards CO1 placed in the field area 54 and the common area 52 in attacks. If the opponent's life points are reduced to zero, the user is declared a winner and the battle ends. Conversely, if the user's life points are reduced to zero by an attack from the opponent's monster card CO1, the user is declared a loser and the battle ends.

[0053] The common area 52 is an area shared by the user and the opponent. The common area 52 can be configured as appropriate, but in the example of FIG. 3, two card placement areas CP are provided. The user's card object CO and the opponent's card object CO are appropriately placed in each card placement area CP. Any card object CO may be placed in each card placement area CP in the common area 52 regardless of its type, but as an example, it is limited to an extra monster card CO1. In other words, an extra monster card CO1 summoned from a summon deck card CO7 is first placed in each card placement area CP in the common area 52, and is only allowed to be placed in the field area 54 if certain field conditions are met. In this example, the user area 51 and each card placement area CP in the common area 52 (which may include the extra deck placement area 56) function as multiple card placement areas of the present invention.

[0054] The opponent area 53 is an area reserved for the opponent. For the opponent, the opponent area 53 plays the same role as the user area 51. For this reason, the opponent area 53 also has 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, but because their roles are the same as those of the user area 51, their explanations will be omitted.

[0055] FIG. 4 is a diagram showing an example of the procedure for playing the card game in the example of FIG. 3. As shown in FIG. 4, the card game includes a user's turn and an opponent's turn (hereinafter, sometimes referred to as the "player" when there is no distinction between the two). The game proceeds in a so-called turn-based format, in which the user and opponent alternate turns through these turns. Specifically, as preparation for the game, for example, the players' deck cards are shuffled and placed in the main deck placement area 55, and a predetermined number of card objects CO are drawn from each player's deck cards (the deck CO6) and displayed (placed) as their hand CO5 in the hand placement area 57. Once preparation is complete, the game begins with the turn of the player going first (for example, the user going first). One turn is divided into multiple 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 merely an example.

[0056] In each phase, the player given a turn can select an appropriate action within the range set for that phase. An example is as follows: In the draw phase, a card object CO6 is drawn from the deck CO6, and in the standby phase, the effect of the card object CO6 designated for effect processing in that phase can be activated. In the first main phase, various actions are permitted using the card objects CO6 in the field area 54 as appropriate, such as summoning various objects such as monsters used in battle, setting card objects CO6 with unique effects such as spells and traps, or activating their effects. In the battle phase, a battle is conducted using card objects CO6. For example, a battle is conducted by selecting a monster card CO6 to be used in the user's attack during their turn and a monster card CO6 to be the target of the opponent's attack (or a direct attack on Life Points if there is no monster card CO6 in the field area 54). The outcome of the battle is determined by parameters such as the attributes and strength of the monster card CO6. In the second main phase, the same actions as in the first main phase are permitted. The end of the turn is announced during the end phase.

[0057] Note that combat during the Battle Phase can be avoided by the player who has been given a turn. In that case, the Battle Phase and the Second Main Phase are skipped. Similarly, the Second Main Phase can also be avoided during the Battle Phase by the player's choice. The end of a phase is indicated by a predetermined ending operation. When a turn ends, the turn passes to the opposing player. As turns are alternated, the game ends when a predetermined victory or defeat condition is met. As an example of the victory or defeat condition, as described above, the victory or defeat condition is met when the life points set for each player P decrease to a predetermined value (for example, 0) through combat.

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

[0059] As shown in FIG. 4, a battle on the battle screen 50 is played by players repeatedly repeating turns including multiple phases. Therefore, options on the battle screen 50 include a phase end command to end each phase (e.g., to enter the next battle phase in the first main phase) and a turn end command to end each turn. Furthermore, on the battle screen 50, deck cards (a group of card objects) are used throughout each phase as described above and placed in the field area 54 or the like via the hand CO5. Therefore, the user is required to select which hand cards CO5 to place in which card area CP and how. For magic cards CO2 and trap cards CO3, the user must also select when and how to activate their effects. For monster cards CO1, the user must also select whether to attack in the battle phase, and if so, which monster card CO1 to attack. When the analysis service is applied to the battle screen 50, information on the relative merits of these options is provided.

[0060] Specifically, as shown in Fig. 5, when an analysis service is applied, an advice display field 60 is additionally displayed compared to the example in Fig. 3. The advice display field 60 is a field for displaying information on the relative merits of each option (analysis results) provided by the analysis service. Whether or not the analysis service is used, in other words, whether or not the advice display field 60 is displayed, may be set appropriately, and may be displayed by a predetermined operation to request its display, but as an example, it is automatically displayed at each selection opportunity without the need for a request from the user.

[0061] The advice display field 60 may display any number of pieces of superiority / inferiority information; for example, superiority / inferiority information for all options may be displayed. In the example of FIG. 5, five pieces of advice information are displayed via five advice units 61, namely, first advice unit 61A to fifth advice unit 61E. The analysis service also uses multiple artificial intelligence models provided in the game server 2, and multiple pieces of superiority / inferiority information (analysis results) for each option are output via these models. Therefore, the advice display field 60 displays multiple pieces of superiority / inferiority information output by the multiple artificial intelligence models for each option. The number (types) of artificial intelligence models may be any number, but in the example of FIG. 5, two types of artificial intelligence models, "offensive AI" and "defensive AI," are used, and two types of superiority / inferiority information are displayed for each option.

[0062] "Offensive AI" and "Defensive AI" indicate an offensive artificial intelligence model (hereinafter sometimes referred to as offensive AI) and a defensive artificial intelligence model (hereinafter sometimes referred to as defensive AI), respectively. The five advice sections 61 in the advice display field 60 are classified into advice sections 61 that display superiority / inferiority information based on offensive AI and advice sections 61 that display superiority / inferiority information based on defensive AI. The advice sections 61 corresponding to offensive AI are displayed in white in the example of FIG. 5, and correspond to, for example, the first advice section 61A, the third advice section 61C, and the fourth advice section 61D. On the other hand, the advice sections 61 corresponding to defensive AI are displayed in gray in the example of FIG. 5, and correspond to, for example, the second advice section 61B and the fifth advice section 61E.

[0063] As described above, each artificial intelligence model is realized by the content of the analytical trained data AD, for example, and each analytical trained data AD is generated by the content of a training dataset. The offensive AI corresponds to a trained artificial intelligence model based on the analytical trained data AD generated using the offensive training dataset, and the defensive AI corresponds to a trained artificial intelligence model based on the analytical trained data AD generated using the defensive training dataset. For example, among the multiple types of analytical model units 23, the first analytical unit 23A corresponds to the offensive AI, and the second analytical model unit 23B corresponds to the defensive AI. The offensive training dataset and the defensive training dataset can be configured as appropriate, for example, by a dataset including a record set of wins achieved with an offensive-focused strategy and a dataset including a record set of wins achieved with a defensive-focused strategy, respectively.

[0064] Each advice section 61 may be configured as appropriate, but in the example of FIG. 5 , it includes an advice information section 62 and an option information section 63. The advice information section 62 is a section that displays detailed information including superiority / inferiority information. The superiority / inferiority information may be any information regarding the influence of each option, such as symbols indicating the degree of influence each option has on victory, such as A, B, and C, or text information that intuitively indicates the influence each option has, such as "likely to lead to victory." In the example of FIG. 5 , however, it is configured as numerical information that indicates the likelihood of progress being advantageous in the battle through numerical magnitude. For example, the first advice section 61A displays numerical information such as "42.0%" as superiority / inferiority information. The numerical information may be a number indicating various advantages, such as a number indicating the likelihood of progress being advantageous (less likely to be disadvantaged) within a predetermined range of turns or choices to be analyzed. In the example of FIG. 5 , however, it is displayed as a probability value indicating the possibility (probability) of ultimately winning the battle.

[0065] The probability value is information indicating the winning rate compared to other options output by the same artificial intelligence model. For example, the probability value for an offensive AI model is set so that the sum of the probability values ​​for all options output by the offensive AI model is 100%. The numerical information may be independent values ​​(the sum of all options does not necessarily equal 100%), such as the evaluation results for each option by the offensive AI model. However, as an example, it is calculated as a probability value that sums to 100%. The detailed information may also include other appropriate information besides probability value information (superiority / inferiority information). In the example of Figure 5, information indicating the artificial intelligence model that output the probability value, such as "Attacking AI Type A Evaluation," is included. Note that in the example of Figure 5, only one type (e.g., "Type A") of offensive AI and defensive AI is used; however, multiple artificial intelligence models of the same type (e.g., offensive AI) based on different performance sets, such as "Type B," may be used to output the probability value.

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

[0067] Also, in the example of FIG. 5, the probability value for the option "Enter Battle Phase" is output only by the offensive AI, but the probability value for the same option is also output by the defensive AI. Specifically, both the offensive AI and the defensive AI output probability values ​​for all options selectable in each situation on the battle screen 50. Therefore, for each selectable option, at least two types of probability values ​​corresponding to the offensive AI and the defensive AI, respectively, are output. The advice display field 60 may be configured to display all of the two types of probability value information for each option, but in the example of FIG. 5, it is configured to display only the top five options (some of the options) with the highest probability values.

[0068] The ranking of probability values ​​may be evaluated for each artificial intelligence model, such as offensive AI and defensive AI, and the top ranking for each may be displayed in the advice display field 60; however, in the example of FIG. 5 , the ranking is evaluated across offensive AI and defensive AI (different artificial intelligence models). For example, if "entering the battle phase" is evaluated as having the highest probability value for the offensive AI, but "entering the battle phase" is evaluated as having a low probability value for the defensive AI, it is possible that only the analysis results for the offensive AI are displayed in the advice display field 60. In this way, only one of the offensive AI and the defensive AI may be displayed in the advice display field 60 as a probability value for the same option; however, as described above, both the offensive AI and the defensive AI output the same probability values ​​for all options, and both may be displayed as advice in the advice display field 60.

[0069] (How to calculate probability values) 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 offensive AI will be described. FIG. 6 is an explanatory diagram illustrating a list of options (actions) that a player can take in a certain situation on a battle screen 50. In the example of FIG. 6, the options (conceptualized by "○") that a player can take are shown in a tree format, with each option branching to the next option. As shown in FIG. 6, the options that a player can take on the battle screen 50 are classified into multiple layers of option groups, and the options in the deepest layer in each tree correspond to the options that can actually be selected. Then, all of the options that can actually be selected are input to multiple artificial intelligence models such as an offensive AI and a defensive AI, and a probability value for each option is output by each of the multiple artificial intelligence models.

[0070] Specifically, the example of Figure 6 shows a group of options that can be selected in a situation where three monster cards CO1 have already been placed in the front row 54A of the field area 54 in the example of Figure 5, three magic cards CO2, etc. have already been placed in the back row 54B, and the player has two cards, an "A monster card" and a "B magic card," in their hand CO5. In this case, the first options available to the player are: use "A monster card," use "B magic card," "phase transition," and "end turn." Therefore, these four options form the first-tier options that can be selected initially.

[0071] Furthermore, when using "A Monster Card" and "B Magic Card," there are further options for how and where to use them, forming a second layer of options. For example, in the example of Figure 5, the front row 54A of the field area 54 is divided into card storage areas 1 through 5, excluding the leftmost one (a card storage area CP where Monster Card CO1 cannot be placed), and proceeding from the leftmost one to the right, respectively. If three Monster Cards CO1 are placed in card storage areas 3 through 5, there are two card storage areas CPs where "A Monster Card" (Monster Card CO1) can be placed: card storage area 1 and card storage area CP. Therefore, these two card storage areas CPs correspond to the options for where to place "A Monster Card." Furthermore, there are two ways to place Monster Card CO1 in a card storage area CP: "summon" (where Monster Card CO1 is placed face-up) and "set" (where Monster Card CO1 is placed face-down). Therefore, for each of the two card storage area CP options, the first card storage area CP and the second card storage area CP, there are two further options: "Summon" and "Set." As a result, there are four options for using "A Monster Cards," corresponding to the two card storage area CPs and the two placement methods. There may also be various other options for Monster Card CO1s depending on the situation. For example, in the case of Extra Monster Card CO1s, it is necessary to specify a predetermined number (e.g., three) of Monster Card CO1s to be removed from the field area 54 (to be moved to the Graveyard area 58) for the summon. In this case, an option may be derived, such as the predetermined number (e.g., three) times the type of Extra Monster Card CO1s times the card storage area CP.

[0072] On the other hand, for example, in the field area 54 shown in FIG. 5, if the back row 54B is divided into the sixth card place CP through the tenth card place CP from left to right, and three magic cards CO2 and the like are placed in the eighth card place CP through the tenth card place CP, then the two card place CPs where a "B Magic Card" (Magic Card CO2) can be placed are the sixth card place CP and the seventh card place CP. Therefore, these two card place CPs correspond to the options corresponding to the placement destination of the "B Magic Card." Furthermore, while a "B Magic Card" can be configured as any magic card CO2, for example, if a "B Magic Card" is a magic card CO2 that has two placement methods for the card place CP, "activate" (the option to activate the effect) and "set" (the option to withhold the activation of the effect), these two placement methods are available. Consequently, there are four options for using a "B Magic Card," corresponding to the two card place CPs and the two placement methods. These four options plus four options corresponding to "A Monster Cards" make up a total of eight options that form the second layer of options.

[0073] If an "A Monster Card" has an effect, further options are generated. For example, if an "A Monster Card" has an effect that requires it to be placed in the card storage CP by "Summoning," there are two further options for the "Summon" placement method: "Activate effect" and "Do not activate effect." Therefore, four options corresponding to these two options x two card storage CPs are generated from the second layer options for "A Monster Cards."

[0074] Similarly, options are derived when two types of effects are set for a "B Magic Card." For example, if a "B Magic Card" has two types of effects, Effect A and Effect B, two additional placement options are available: "Activate Effect A" and "Activate Effect B." Therefore, four options corresponding to these two options multiplied by two card placement CPs are generated from the second-tier options for the "B Magic Card." These four options, plus four options derived from the second-tier options for the "A Monster Card," form a total of eight options, forming the third-tier options. Note that various other options may also be generated for the Magic Card CO2 depending on the situation. For example, if the target of the effect can be specified, an additional option for selecting that target may be generated. While not shown in the example in Figure 6, the same applies to other card objects CO, such as the Trap Card CO3.

[0075] Furthermore, the fourth layer of options is derived from the third layer of options. For example, if two of the three monster cards CO1 (e.g., "C Monster Card" and "D Monster Card") already placed in the card space CP can be targets for the effect of the "B Magic Card," these two targets are derived as options. Therefore, the eight options corresponding to these two targets x four options (two types of effects that occur for each of the two card space CPs) form the fourth layer of options.

[0076] In the example of Figure 6, in the tree of options derived from "A Monster Card," part of the second-layer options (when placed in a "set" state in each card storage CP) and the third-layer options correspond to the options in the deepest layer. Therefore, each option in these option groups is input into an artificial intelligence model such as an offensive AI, and a probability value (number in "○") is output for each option. Similarly, in the tree of options derived from "B Magic Card," part of the second-layer options (when placed in a "set" state in each card storage CP) and the fourth-layer options correspond to the options in the deepest layer. Therefore, each option in these option groups is input into an artificial intelligence model such as an offensive 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 options group corresponds to the deepest layer options group. For this reason, the two options "Phase Transition" and "Turn End" are input into an AI model such as an offensive AI, and a probability value is output for each option. Note that the example in Figure 6 shows probability value information output by one type of AI model out of multiple AI models as an example, but in reality, multiple probability value information is output for each option by each of the multiple AI models.

[0078] Furthermore, the example of FIG. 6 shows an example of a set of options in the battle phase, which, in addition to a phase end instruction, shows the card object CO to be placed from the hand CO5 in each card place CP, and the options corresponding to the card place CP to which the card object CO should be placed. However, options corresponding to the phase are prepared for each phase, such as adding (drawing) a card from the deck CO6 to the hand CO5 in the draw phase. Therefore, at least some of the options available for each phase (for example, the end of the phase or the end of the turn may be the same) are different. In other words, the options available for each phase are different. Then, information on the probability value of each option available for each phase is output. In this example, the set of options at the deepest level functions as an example of the multiple options of the present invention. Furthermore, the set of options for each phase functions as two or more options of the present invention.

[0079] FIG. 7 is an explanatory diagram illustrating an example of a method by which an artificial intelligence model calculates a probability value. Generally, an artificial intelligence model is generated by machine learning a training dataset using a predetermined learning method (algorithm), and tends to have a way of thinking (algorithm) that corresponds to the learning method. Learning methods in machine learning include various methods, such as imitation learning and reinforcement learning (imitation learning can also be considered a type of reinforcement learning). Imitation learning is often classified as a method such as behavior cloning, dataset aggregation, or inverse reinforcement learning, but the reward is often not explicitly defined. On the other hand, reinforcement learning is often classified as a method such as dynamic programming (DP), Monte Carlo (MC), or temporal difference learning (TD), and is often a method that maximizes the reward.

[0080] The AI ​​model for the analysis service (analysis program AP) may be generated by various learning methods, for example, the TD method, which is a type of reinforcement learning. The TD method may include methods such as SARSA, but for example, the Q-learning method is used to generate the AI ​​model for the analysis service.

[0081] Q-learning is a method for evaluating actions (options) by calculating an action-value function (Q-function). A Q-function is generally defined as a function that predicts the future reward (often called a Q-value) that will result from performing a certain action in a certain state (the specific function formula will be omitted). However, with Q-learning, when the Q-function table (input information) becomes large (huge), the calculations tend to be insufficient and it becomes difficult to implement. On the other hand, for example, the input information (information in the situation log SL) on the battle screen 50 is expected to be enormous. For this reason, a trained AI model for the analysis service is generated using the DQN (Deep Q Network) method, which uses a neural network to obtain an approximation of the Q-value. In this case, the AI ​​model for the analysis service is configured to calculate a probability value (Q-value) using the DQN method.

[0082] Furthermore, the reward (a predetermined result obtained by an action, which may be included in the algorithm of the learning program) that is the subject of the Q value may be set as appropriate, and a specific way of winning, such as a narrow victory, or a tendency for winning, may be set. The AI ​​model for the analysis service may have different thoughts depending on the reward (predetermined result) in Q-learning, but winning a match is set as an example of a reward, and the AI ​​model for the analysis service is configured to calculate the probability value (Q value) for winning a match using the DQN method.

[0083] The example in Figure 7 shows an overview of the DQN method for calculating probability values ​​(Q values). As shown in Figure 7, the DQN method uses a neural network (deep learning), which forms an input layer, a hidden layer, and an output layer, and each layer cooperates to output calculation results for data. The input layer is responsible for collecting data. The hidden layer is responsible for calculations to calculate probability values. Hidden layers often include multiple layers (generally, the more layers there are, the higher the accuracy tends to be). While only two hidden layers are shown in the example in Figure 7, any number of hidden layers may be formed. The output layer is responsible for outputting the results of calculations performed in the hidden layers. The AI ​​model trained using the DQN method is configured to output the probability value of winning a match in the output layer. Furthermore, connection lines (often called synapses) are provided between the input layer, hidden layer, and output layer. Each connection line is assigned a weight (often expressed as the symbol w) that indicates importance (strength of connection), and the importance of the information is determined based on the magnitude of the weight value. In the example of FIG. 7, input values ​​(input information) and output values ​​(which may generally be called nodes) are both represented by "◯".

[0084] Specifically, information from the situation log SL, which indicates the current state of the battle screen 50, is first input to the input layer. The situation log SL contains information on many dimensions (input values). It is desirable that the number of dimensions be less than approximately 5,000. As described above, the situation log SL may contain various information for determining the status of the card game. For example, in addition to information on the placement status in the field area 54, such as the status of the first card place CP to the tenth card place CP, it also contains information on possible options such as end of turn, summoning an A monster card, activating the effect of an A monster card, and activating a B magic card (see, for example, the example in Figure 6).

[0085] A predetermined function formula 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 using this function formula. Any number of output values ​​can be calculated in the first intermediate layer, and four output values ​​are calculated in the example of Figure 7. A predetermined function formula using appropriate weighting is also applied to the four output values, and the output values ​​of the intermediate layer (second layer) are calculated using this function formula. Any number of output values ​​can be calculated in the second intermediate layer, and four output values ​​are also calculated in the example of Figure 7. The weighting (weight value w) corresponding to each connection line is calculated as a learning result and is managed, for example, in the learned data for analysis AD. Also, the connection lines (synapses) connecting the input layer and intermediate layer are omitted as appropriate in Figure 7.

[0086] The output value of the final intermediate layer (second layer) is calculated as a Q value using a predetermined function formula with appropriate weighting in the output layer. The Q value (output value of the output layer) is calculated for each predetermined action (option on the battle screen 50). For example, in each tree in the example of FIG. 6, a Q value is calculated for each option (selectable option) in the option group at the deepest layer using the method shown in FIG. 7. The calculated Q value is then converted into a probability value using a normalization function (e.g., a Softmax function) so that the sum of the values ​​of all selectable options equals 100%. As an example, this method calculates probability values ​​for all selectable options for each situation. Similar calculations are also performed by multiple artificial intelligence models, such as offensive AI or defensive AI (e.g., the first analysis program AP1 and the second analysis program AP2). The top probability values ​​(e.g., the five in the example of FIG. 5) calculated by the multiple artificial intelligence models are then provided to the user via the advice display field 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 omitted as appropriate.

[0087] (guessing service) Next, the details of the guessing service will be described with reference to FIG. 8. The guessing service is a service that provides guess information for guessing the contents of an opponent's elements (e.g., card objects CO) that are not shared with the user, among a group of elements (e.g., card objects CO) prepared for each player in a card game. The guessing service can be applied to any situation involving elements that are kept secret from the user, but is applied to guessing card objects CO, such as card objects CO, hand cards CO5, deck cards CO6, or summon deck cards CO7, placed face down in a hidden field area 54 on the battle screen 50. In other words, on the battle screen 50, guess information for guessing the contents of card objects CO, etc. placed face down is provided via the guessing service. FIG. 8 schematically illustrates an example of a battle screen 50 when the guessing service is applied. Note that in the example of FIG. 8, components similar to those of the battle screen 50 in FIG. 3 are designated by the same reference numerals, and their description will be omitted.

[0088] As shown in FIG. 8 , when the guessing service is applied, a guess information section 70 is additionally displayed on the battle screen 50 compared to the example of FIG. 3 . The guess information section 70 is a section for displaying guess information. The guess information section 70 may be configured as appropriate, but in the example of FIG. 8 , it includes a target designation section 71, a candidate image 72, and a candidate arrangement line 73. The target designation section 71 is a section indicating the target (guessing target) for which guess information is displayed. The target designation section 71 may be configured as appropriate as long as it allows the user to identify the target, but in the example of FIG. 8 , it is indicated by a dashed line surrounding the guessing target card object CO (a card object CO whose face-up side is hidden). Furthermore, the guess information section 70 may be displayed to target appropriate card objects CO as guess targets. For example, it may be displayed to target all face-down card objects CP or an appropriate portion of them. In the example of FIG. 8 , it is displayed to target the face-down card object CP designated (selected) by the user. In this case, the target designation unit 71 has a function of distinguishing the card object CP designated by the user as the target of guessing from other card objects CP facing down.

[0089] The candidate images 72 are images that represent candidates for the card object CO to be inferred. More specifically, an image of the front side (contents) of a card object CO that is inferred to correspond to the card object CO to be inferred is displayed as the candidate image 72. Although only the most likely candidate card object CO may be displayed as the candidate image 72, in the example of FIG. 8, two candidate images 72 ("A" and "B") corresponding to the top two most likely candidates (this is just an example, and any appropriate number may be used) are displayed. Furthermore, a numerical value indicating the probability (hereinafter, sometimes referred to as a matching probability value) is displayed above each candidate image 72. For example, the numerical value "56%" is displayed above the candidate image 72 corresponding to "A," and the numerical value "35%" is displayed above the candidate image 72 corresponding to "B." These matching probability values ​​are output by a trained artificial intelligence model for the inference service (inference program FP) based on the situation log SL.

[0090] The candidate arrangement line 73 is a line that indicates the relationship between the target designation section 71 and each candidate image 72. Each candidate image 72 is arranged below the candidate arrangement line 73 along the line 73. Therefore, the candidate arrangement line 73 also functions as a reference for lining up each candidate image 72 side by side.

[0091] (Method of calculating estimated information) Next, with reference to FIG. 9, a method for calculating inferred information (candidates and match probability values) output by a trained artificial intelligence model for an inference service will be described. FIG. 9 is an explanatory diagram illustrating an example of a method by which an artificial intelligence model calculates inferred information. The example in FIG. 9 shows a case in which inferred information is provided on a battle screen 50. The artificial intelligence model for an inference service may also be trained by an appropriate learning method, but the example in FIG. 9 shows a case in which it is generated using a neural network (deep learning) method. As shown in FIG. 9, in this case, as in the calculation of probability values, the inferred information is calculated using an input layer, an intermediate layer, and an output layer. Specifically, in the input layer, information indicating the situation on the battle screen 50 is input as input information (input value) to the trained artificial intelligence model for an inference service. Information different from that used in the calculation of probability values ​​may be used as input information, but as an example, a similar situation log SL (only a portion of which 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 function formula that uses appropriate weighting (weight values), but the weight values ​​and function formula used there are different from those in the artificial intelligence model for the analysis service. The weight values ​​are defined by the learned data for prediction FD (data generated by the above-mentioned learning), and the function formula is configured to predict the contents of the face-down card object CO. In calculating the prediction information, any number of intermediate layers can be formed, but in the example of Figure 9, two intermediate layers are formed. Similarly, any number of output values ​​(nodes) can be provided in each intermediate layer, but in the example of Figure 9, four output values ​​are provided in each intermediate layer.

[0093] In the output layer, a value indicating the possibility that the card object CO corresponds to the card object CO to be guessed is calculated for each card object CO from the output value of the final intermediate layer (second layer). The card objects CO for which the possibility is calculated may be all types that can be used in a card game (the opponent's deck cards do not need to be taken into consideration), but as an example, they are limited to card objects CO that the opponent has incorporated into his deck cards. This limitation may be realized as appropriate; for example, if a learning dataset containing only the opponent's deck cards as performance samples is prepared in advance and a trained artificial intelligence model (the guess model unit 24) that has trained this learning dataset is provided, the limitation may be realized by using the artificial intelligence model, but as an example, after the guess candidates are calculated, the limitation may be realized by excluding (filtering out) cards other than the opponent's deck cards from the candidates.

[0094] Furthermore, in a card game, rules are appropriately reflected. For example, the rules may limit the number of card objects CO of a particular type that can be used in a deck, such as limiting the number of cards that can be used to three. In this case, if all three card objects CO of that particular type are already open (for example, placed face up in the field area 54, etc.), the card objects CO of that particular type are excluded (filtered) from the targets of the guess information.

[0095] Furthermore, rule changes may occur from time to time (including regular changes such as once a year, as well as irregular changes such as temporary changes to respond to changing circumstances). For example, the number of cards that can be used may change, such as changing the number of a certain type of card object CO that can be included in a deck from three to two. Similarly, rule changes may be implemented from time to time, such as limit regulations (restrictions that make certain card object COs unusable). For example, if a card object CO that is too strong exists, that card object CO may always be included, which could lead to a bias in deck cards. Alternatively, this could lead to a sense of unfairness in the results of matches. For this reason, for example, if a certain card object CO is banned from use, that particular card object CO is also excluded (filtered) from the calculation of possibilities. Alternatively, if the number of cards that can be used is changed to two, that card object CO is excluded from the calculation of possibilities once both cards are revealed (or, if the limit is increased, it is postponed from being excluded from the calculation until the limit is reached).

[0096] In a card game, various changes, such as changes in the number or types of usable card objects CO (elements), may occur in response to rule changes, etc., as described above. These changes are then reflected in the targets of the speculation information. Specifically, the range for which the value indicating the possibility is calculated (in other words, the card objects CO that are candidates for the hidden card object CO) is limited to card objects CO that are usable under the current rules and that the opponent has included in their deck cards.

[0097] Furthermore, the values ​​indicating the possibilities in the output layer (the values ​​of each candidate after being limited to usable candidates by the filter) are normalized using a normalization function (e.g., a Softmax function) so that the sum of the values ​​of all selectable options (e.g., all card objects CO included in the opponent's deck) is 100%. The normalized value is then calculated as guess information (match probability value). As an example, using this method, candidate card objects CO and match probability values ​​are calculated for each guess target card object CO (a card object CO whose contents are concealed) by a trained artificial intelligence model for the guess service. Then, among the calculated candidate card objects CO, those with the highest match probability values ​​(e.g., the two in the example of FIG. 8) are provided to the user via the guess information unit 70. Furthermore, when the information input in the input layer includes executable actions (options), even if the situation is the same, the way in which they were used to reach the current situation (board state) is taken into consideration. This allows for the calculation of guess information with high accuracy.

[0098] (Type of inferred information) Next, with reference to FIGS. 10 and 11, the types of inferred information that can be provided through the inference service will be described. While only one type of trained artificial intelligence model may be used for the inference service, as an example, multiple types of artificial intelligence models (e.g., a first inference program FP1 and a second inference program FP2) are prepared. Each artificial intelligence model outputs inferred information based on different inference-use trained data FD. Because different artificial intelligence models may reach the same conclusion, i.e., the same inferred information, multiple types of artificial intelligence models do not necessarily output different inferred information. However, in many cases, multiple types of inferred information are output. FIG. 10 is an explanatory diagram for explaining an example of the types of inferred information output by multiple types of artificial intelligence models. Furthermore, each artificial intelligence model may output various types of inferred information as appropriate. In the example of FIG. 10, (1) recommended information by period and (2) recommended information by rank are shown. Furthermore, while appropriate recommended information (including all) from among these multiple pieces of recommended information may be displayed, in the example of FIG. 10, only some of the recommended information selected by the user is displayed in both (1) and (2).

[0099] The recommended information for each period is recommended information output by a trained artificial intelligence model based on the trained data for estimation FD for each period generated using the training data set for each period. In this case, the multiple trained data for estimation FD are generated based on multiple training data sets (each including performance samples for multiple periods). The inference information for each period may be provided as appropriate, but as an example, is switched by a user instruction (selection). The user instruction may be executed as appropriate, but the example in FIG. 10 shows a case where it is executed via the information switching unit 80.

[0100] On the other hand, the recommended information for each rank is output by a trained artificial intelligence model based on the trained data for estimation FD for each rank generated using the training data set for each rank. Card games are provided with a function for ranking players (information indicating the player's skill) based on their performance, and the recommended information for each rank corresponds to the output result of an artificial intelligence model generated from a training data set including performance samples of the same rank. In this case, multiple trained data for estimation FDs corresponding to multiple training data sets (each including performance samples of multiple ranks) are prepared. The estimated information for each rank may be provided as appropriate, but as an example, it is provided so that it can be switched by a user's instruction via the information switching unit 80, similar to the estimated information for each period. In the example of FIG. 10, the estimated information unit 70 is enlarged and schematically shown when the battle screen 50 includes the information switching unit 80 in both (1) and (2).

[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 unit for displaying the type of inferred information currently being displayed. The first switching unit 82 and the second switching unit 83 are both units for indicating a position where an instruction (for example, a touch operation) for switching the type of inferred information being displayed should be executed. When a touch operation is executed on the first switching unit 82 or the second switching unit 83, the currently displayed object is switched to the previous display object and the next display object, respectively.

[0102] Specifically, as shown in (1) of FIG. 10, when inferred information for each period is provided, the type of inferred information is expressed, for example, by information indicating a period, such as "23.1-23.2." The information "23.1-23.2" (indicating a one-month period from January to February 2023) indicates the period during which performance samples included in the training dataset from which the information was generated were collected. On the other hand, when a touch operation is performed on the first switching unit 82, the display target is switched to a period prior to the currently displayed target period (e.g., a one-month period from December to January 2022). In other words, the display target is switched to inferred information (candidate images 72 including matching probability values) output by an artificial intelligence model generated using multiple training datasets each including performance samples from the previous period. Similarly, when a touch operation is performed on the second switching unit 83, the display target is switched to a period subsequent to the currently displayed target period (e.g., a one-month period from February to March 2023). In the example of FIG. 10, the range of the display target is divided into one-month intervals, but this is not limitative and may be set as appropriate.

[0103] Furthermore, as shown in (2) of FIG. 10, when inferred information for each rank is provided, the type of inferred information is represented by information on "second rank" indicating the rank. The information on "second rank" indicates the rank of the player from whom performance samples included in the training dataset from which the inferred information was generated were collected. In this case, it means that the inferred information was generated from multiple training datasets, each of which includes performance samples of only players of the second rank. On the other hand, when a touch operation is performed on the first switching unit 82, the display object is switched to a rank lower than the rank of the currently displayed object (e.g., the first rank). In other words, the display object is switched to inferred information (candidate image 72 including a matching probability value) output by an artificial intelligence model generated using multiple training datasets, each of which includes performance samples of only players of the first rank, which is one rank lower than the current second rank. Similarly, when a touch operation is performed on the second switching unit 83, the display object is switched to a rank higher than the rank of the currently displayed object (e.g., the third rank).

[0104] In the example of FIG. 10, the range of display objects is divided into ranks, but is not limited to this and may be an appropriate range (for example, two or more ranks), in which case information indicating the range may be added. The initial display object (default display) may be set appropriately, for example, to the user's own rank or the opponent's rank. In this case, estimated information tailored to the user's own or the opponent's rank is displayed by default, thereby improving usability. The initial display for a period may also be set appropriately, for example, to the most recent period or the most frequently used period.

[0105] Each artificial intelligence model can output various types of inferred information based on differences in algorithms, etc.; however, as described above, in the example of Figure 10, multiple types of inferred information are output based on differences in the inferred trained data FD. In card games, trends may occur in the types of card objects CO used (deck card combinations). When trends occur, an AI model that reflects those trends is likely to output more accurate inferred information. Trends often occur over time. Therefore, if inferred information for each time period is provided, trends can be reflected in the inferred information, thereby enabling more accurate inferred information. Similarly, with card objects, differences often occur in the card objects CO incorporated into card decks and how they are used depending on their rank (player's skill). Therefore, if inferred information for each rank is provided, usage trends for each rank can be reflected in the inferred information, thereby enabling more accurate inferred information.

[0106] Note that the multiple training data sets are not limited to the above-mentioned training data sets for each period or each rank, and any appropriate training data set including results collected from various perspectives, such as rules (regulations), number of usable cards, etc. For this reason, for example, a training data set including performance samples for all card objects CO included in a card game (all without being limited by period or rank) may be used. In other words, the multiple trained data for inference FD may be data generated from various training data sets that provide appropriate thinking to the inference program.

[0107] FIG. 11 is an explanatory diagram illustrating training datasets for realizing inferred information for each period and for each rank. The inferred information for each period and for each rank may be prepared separately as independent inferred information, and may be realized by separate training datasets, such as multiple training datasets for each period or multiple training datasets for each rank. Alternatively, inferred information that takes both into consideration may be prepared, such as preparing inferred information for each period for each rank. The example of FIG. 11 shows types of training datasets when inferred information that takes both into consideration is prepared.

[0108] As shown in FIG. 11, when inferred information that takes both factors into account is prepared, types of training datasets corresponding to the number of periods x the number of ranks are prepared. Specifically, a training dataset corresponding to the first rank (including performance samples of players in the first rank) is prepared for each period, such as a "first training dataset" corresponding to the period "2023.1-2023.2" and a "second training dataset" corresponding to the period "2023.1-2023.2" (both training datasets include performance samples of players in the corresponding period). In this case, for example, an artificial intelligence model generated using the "first training dataset" outputs inferred information that takes into account (reflects) both the trends of players in the first rank and the trends of deck cards that were popular from January to February 2023.

[0109] The same applies to the second and third ranks, where training data sets for each period, such as "third training data set" to "sixth training data set," are prepared. An artificial intelligence model based on these will then output inferred information that takes into account (reflects) the trends of each rank and each period. Note that while the example in FIG. 11 shows first to third ranks, a card game may have any number of ranks, and training data sets corresponding to that number may be prepared. The same applies to periods.

[0110] (Network system processing) Next, with reference to FIGS. 12 to 14, the choice analysis process, display change process, and behind-the-scenes inference process will be described as examples of processes performed by the network system 1. The choice analysis process is a process for providing multiple types of superiority / inferiority information for each choice in a selection opportunity by using multiple types of artificial intelligence models. The choice analysis process may be configured to provide multiple types of superiority / inferiority information by using multiple types of artificial intelligence models in parallel, but the example in FIG. 12 shows a case where multiple types of artificial intelligence models are used in series to provide multiple types of superiority / inferiority information. Furthermore, the choice analysis process is realized by cooperation between the analytical model unit 23 of the game server 2 and the model processing unit 35 of the user device 3, but in the example in FIG. 12, the process mainly executed by the analytical 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.

[0111] The model processing unit 35 starts the option analysis process of FIG. 12 when a predetermined analysis time (e.g., a user's turn on the battle screen 50 or each phase) arrives, and first requests the game server 2 to analyze each option at the selection opportunity (step S101). This request may be executed appropriately, for example, by the analytical model unit 23, and may be executed so that the analytical model unit 23 outputs the analysis results of each analytical model unit 23, such as the first analytical model unit 23A and the second analytical model unit 23B, in a predetermined order. As an example, the request is executed by one of the analytical model units 23, such as the first analytical model unit 23A and the second analytical model unit 23B, in a predetermined order. Specifically, in step S101, the model processing unit 35 first requests the first analytical model unit 23A to analyze each option. Furthermore, this request includes a situation log SL indicating the situation at the time of the request.

[0112] When an analysis request is transmitted, the first analytical model unit 23A starts the choice analysis process of FIG. 12 and first acquires the request (step S201). Next, the first analytical model unit 23A performs an analysis of each choice based on the situation log SL included in the acquired request (step S202). This analysis may be performed for only some of the choices in the selection opportunity, but as an example, it is performed for all choices. After performing the analysis, the first analytical model unit 23A outputs the analysis results (step S203) and transmits them to the model processing unit 35 (step S204). Then, after transmitting the results, the first analytical model unit 23A terminates the current choice analysis process.

[0113] On the other hand, when the analysis results are transmitted from the first analytical model unit 23A, the model processing unit 35 acquires the analysis results and determines whether the number of types of acquired analysis results (in other words, artificial intelligence models) has reached a set number (for example, two, offensive AI and defensive AI) (step S102). If the number of types of acquired analysis results has not reached the set number (step S102: No), the model processing unit 35 returns to step S101 and requests analysis from the next analytical model unit 23 (second analytical model unit 23B) in a predetermined order (step S101).

[0114] 12, and first acquires the request (step S201). Then, similar to the first analytical model unit 23A, the second analytical model unit 23B executes an analysis of each option (step S202), outputs the analysis results (step S203), and transmits them to the model processing unit 35 (step S204). After the transmission, the second analytical model unit 23B terminates the current option analysis process.

[0115] On the other hand, if the number of types of acquired analysis results reaches the set number (step S102: Yes), the model processing unit 35 extracts an analysis result (superiority / inferiority information) to be displayed from all acquired analysis results (including analysis results from multiple types of analytical model units 23) (step S103). The analysis result to be displayed can be set as appropriate, but as an example, it is set to an analysis result indicating the top five probability values ​​across analysis results from multiple types of analytical model units 23. Therefore, in step S103, the model processing unit 35 extracts, as the display target, the analysis results corresponding to the top five probability values ​​from all analysis results. Next, in step S104, the model processing unit 35 displays, on the battle screen 50, an advice display column 60 including, as advice fields 61, the five extracted analysis results. After this display, the model processing unit 35 terminates the current choice analysis process. As a result, an analysis service is realized on the battle screen 50 via the advice display column 60. More specifically, a battle screen 50 is realized that includes an advice display section 60 that carefully selects and presents the top five probability values ​​for each option, each of which is output from a plurality of artificial intelligence models.

[0116] The display change process is a process for changing the display mode of each analysis result in the advice display field 60, such as the display target or the sorting order. The display mode of the advice display field 60 may be fixed, for example, the top five in order of probability value, or may be variable and change according to a user specification. When a user instructs the model processing unit 35 to change the display mode of the advice display field 60, the model processing unit 35 starts the display change process of FIG. 13 and first determines the conditions specified by the user (step S301). The user may be allowed to specify any desired settings, but are limited to pre-prepared conditions, for example. The pre-prepared conditions may include appropriate conditions, such as filter conditions and sort conditions.

[0117] The filter conditions are conditions for limiting the analysis results to a portion of the display target. The filter conditions may be various conditions for limiting the display target to an appropriate portion, including, for example, limiting the display target to the analysis results of a specific artificial intelligence model (if three or more types of analytical model units 23 are provided, this may be two or more types of analytical model units 23) based on a user specification. The sort conditions (rearrangement conditions) are conditions for rearranging the analysis results in a predetermined order specified by the user. For this reason, in step S301, the model processing unit 35 determines conditions such as filter conditions or sort conditions as specified conditions. In the case of filter conditions, the model processing unit 35 also determines the specific artificial intelligence model specified as the filtering target as a specified condition. On the other hand, in the case of sort conditions, the model processing unit 35 also determines the predetermined order specified as the sort condition as a specified condition. Both filter conditions and sort conditions may be specified, in which case both are determined.

[0118] Next, the model processing unit 35 determines the analysis results (superiority / inferiority information) of the target corresponding to the specified condition determined in step S301 from all the analysis results output by the analytical model unit 23 (all the analysis results acquired in step S102 of the option analysis processing in FIG. 12) (step S302). Specifically, the model processing unit 35 determines the analysis results output by a specific artificial intelligence model (a specific type of analytical model unit 23, such as the first analytical model unit 23A or the second analytical model unit 23B) specified as the filter condition from among all the analysis results. Alternatively, the model processing unit 35 determines each analysis result corresponding to a predetermined sort order. The predetermined sort order may be appropriately specified, and may be, for example, an order by artificial intelligence model, such as the second analytical model unit 23B (defensive AI) or the first analytical model unit 23A (offensive AI), an order by lowest probability value, a top order within a predetermined range of probability values, or an order by the sum of probability values ​​of multiple artificial intelligence models (which may be highest or lowest order, as appropriate).

[0119] Next, the model processing unit 35 changes the display of the advice display field 60 to display the analysis results of the target determined in step S302 according to the specified conditions (step S303). For example, if the analysis results of the first analytical model unit 23A (e.g., an aggressive AI) are specified as the filter conditions, the display of the advice display field 60 is changed to display only the analysis results of the first analytical model unit 23A. If the sorting condition specifies a sorting order for each artificial intelligence model, the display of the advice display field 60 is changed to arrange the advice units 61 for each artificial intelligence model. The same applies when a sorting order is specified, such as ascending order of probability values, ascending order of probability values ​​within a predetermined range, or the sum of probability values ​​from multiple artificial intelligence models. In these cases, the number of display targets may be changed as appropriate, but is set to five, as before the change, for example. Therefore, the model processing unit 35 changes the display of the advice display field 60 to display advice units 61 corresponding to the top five probability values ​​of the first analytical model unit 23A or the top five probability values ​​in the predetermined sorting order. After this change, the model processing unit 35 terminates the current display change process. As a result, the display of the advice display field 60 is changed so that the analysis results specified by the user are displayed in the specified order or the like.

[0120] The back-side guessing process is a process for using a trained artificial intelligence model to provide guess information about the opponent's back-side card object CO (information not shared with the user) on the battle screen 50. The back-side guessing process is realized by the cooperation of the guessing model unit 24 of the game server 2 and the model processing unit 35 of the user device 3, but in the example of Fig. 14, the process executed mainly by the guessing model unit 24 is shown as the game server 2, and the process executed mainly by the model processing unit 35 is shown as the user device 3.

[0121] The model processing unit 35 starts the back-side guessing process of FIG. 14 when a predetermined start condition is satisfied (for example, when a face-down card object CO is selected by a touch operation or the like by the user on the battle screen 50), or when an instruction to switch the type of guess information is given via the first switching unit 82 or the like of the information switching unit 80, and first requests the game server 2 to guess the target face-down card object CO on the battle screen 50 (the card object CO selected via a touch operation, or the card object CO selected before the instruction to switch) (step S401). This request includes a situation log SL indicating the situation at the time of the request. Furthermore, when an instruction to switch the type of guess information is given via the first switching unit 82 or the like of the information switching unit 80, the request also includes information on the type of guess information specified by the instruction.

[0122] When an inference request is transmitted, the inference model unit 24 starts the back-side inference process of FIG. 14 and first acquires the request (step S501). Next, the inference model unit 24 infers the contents of the face-down card object CO based on the situation log SL included in the acquired request (step S502). When an instruction to switch the type of inference information is issued via the first switching unit 82 or the like of the information switching unit 80, this inference is executed by the inference model unit 24 of the type corresponding to the instructed type, such as the first inference model unit 24A or the second inference model unit 24B. Then, the inference model unit 24 outputs the inference results of the designated artificial intelligence model, such as the first inference model unit 24A (step S503). In this case, the inference model unit 24 may output only the number of inference results to be displayed as candidate images 72 (e.g., the top two), but as an example, the inference model unit 24 outputs the inference results including information on all candidates. In addition, the estimation model unit 24 may output estimation information regarding all of the back-side card objects CP included in the battle screen 50 as estimation results (in this case, if a target other than the currently specified target is newly specified as a target to be estimated later, the user device 3 may extract and display the estimation information of the newly specified target from the estimation information already output), but as an example, only the estimation information regarding the currently specified back-side card object CP is output.

[0123] Next, the inference model unit 24 adjusts the inference result (step S504). The adjustment can be implemented as appropriate, but as an example, it is performed to reflect rules such as limit regulations and the opponent's deck cards. For example, the inference model unit 24 adjusts the inference result so as to exclude from the output inference result any card objects CO that have been banned from use and any card objects CO that the opponent has not included in his deck cards. This exclusion may also be performed by simply excluding the banned card objects CO from the candidates output as the inference result. As an example, it is performed by recalculating (adjusting) the match probability value after the exclusion. Thereafter, the inference model unit 24 transmits the adjusted inference result to the model processing unit 35 as inference information (step S505). After this transmission, the inference model unit 24 terminates this back-side inference process. Note that the processing of step S504 may be omitted as appropriate when adjustment is unnecessary (including when adjustment is not performed at all), such as when processing is performed by an artificial intelligence model that has trained on a learning dataset limited to the adjusted target (when similar results can be obtained without adjustment), as described above.

[0124] On the other hand, when the inferred information is transmitted from the inferred model unit 24, the model processing unit 35 acquires the inferred information (step S402). Next, the model processing unit 35 identifies inferred information to be displayed from the acquired inferred information (step S403). For example, if the number of images to be displayed as candidate images 72 in the inferred information unit 70 is the top two in the matching probability value, the model processing unit 35 identifies the inferred information with the top two matching probabilities from the acquired inferred information as the display object. Next, the model processing unit 35 displays the inferred information to be displayed identified in step S404 on the battle screen 50 (step S404). Specifically, the inferred information unit 70 including the inferred information identified in step S404 is displayed on the battle screen 50. Then, after this display, the model processing unit 35 ends the current behind-the-scenes inference process.

[0125] 14, a guessing service is realized on the battle screen 50 via the guess information unit 70. More specifically, the battle screen 50 is realized, which includes the guess information unit 70 that carefully selects and provides the top two guess information for each face-down card object CO (e.g., information including the contents of candidate card objects CO and the probability of matching) output from the artificial intelligence model as information for guessing the contents of the face-down card object CO. Note that the display targets or the order of the guess information unit 70 may be changed as appropriate, and in that case, the display targets, etc. may be changed, for example, by a procedure similar to that of the display change process in the example of FIG. 13 (however, a procedure targeting the guess information and the guess information unit 70 instead of the analysis result and advice display column 60).

[0126] As explained above, according to this embodiment, multiple probability values ​​(superiority / inferiority information) indicating the probability of winning the battle for each option on the battle screen 50 are output by multiple types of trained artificial intelligence models (first analysis model unit 23A, second analysis model unit 23B) via the analysis service (which may be information indicating various possibilities for advantageous progress within a predetermined range of the analysis target), and information on the top five probability values ​​among these is provided before an option is selected. Therefore, it is possible to provide probability value information (superiority / inferiority information) from various perspectives for each option of selection, such as which card object CO should be placed in which card place CP in order to win the battle, or whether to end a phase, that is, numerical information indicating the magnitude of the influence on victory in the game.

[0127] In card games, players are given turns that alternate, each containing multiple phases. Each phase provides a choice opportunity, but the end of the phase and the end of the turn are also included in the set of options for each choice opportunity. When a turn change or other event functions as one of the options, the impact of a choice made during a choice opportunity on progress tends to become more complex. When each turn includes multiple choice opportunities across multiple phases, and each of these choice opportunities ends selectively, the impact is likely to become even more complex. Since each choice opportunity requires a complex choice involving many options (including options derived from each option), even if turn instructions or other factors are not selective, complex choices are still required. Furthermore, information on multiple probability values ​​can be provided from various perspectives regarding such complex choices. Furthermore, information on multiple probability values ​​can be limited or appropriately sorted using filter conditions or the like. This improves the convenience of accessing specific probability value information (option information) through filter conditions or the like.

[0128] On the other hand, through the guessing service, information about the opponent's card object CO (element) that is not shared with the user, for example, the content of the card object CO that is concealed from the user by being placed face down, is guessed by a trained artificial intelligence model (the guessing model unit 24), and the guessed result is provided as guessed information. Therefore, the guessed information can help guess the concealed card object CO in an incomplete information game.

[0129] Furthermore, in a card game, a user uses a deck card selected by the user from among all card objects CO owned by the user. Therefore, the card game is configured as a competitive game in which there is a difference in assets between players. In this case, it is highly likely that it is more difficult to guess the hidden card object CO than when all element groups are used. Similarly, in a card game, the available card objects CO may change depending on the time of year, etc., which also makes it highly likely that it is more difficult to guess the hidden card object CO than when the available card objects CO are fixed (unchanging). Therefore, while an experienced player may be able to guess the contents of a hidden card object CO by looking at it face down, such guessing is generally difficult, and is often particularly difficult for beginners. Even when it is difficult to guess the hidden card object CO, the guess information can help with the guess. Therefore, the guess information can help close the gap with experts (eliminate the gap). Furthermore, when changes in the available card objects CO are reflected in the guess information, the accuracy of the guess information can be improved compared to when similar changes are not reflected. Therefore, the guess information can further help eliminate the gap with experts.

[0130] Furthermore, multiple pieces of inferred information are provided by multiple trained AI models according to time and rank. In card games, trends in the types of card objects (element groups) used may occur. When trends exist, an AI model that reflects those trends is likely to output more accurate inferred information. Trends often occur over time. Similarly, the types and usage methods of card objects CO incorporated into the card object groups often vary depending on rank (user skill). Therefore, by training a learning dataset including performance data for a predetermined period, trends over that period can be reflected in the inferred information. Alternatively, by training a learning dataset including performance data for each rank, usage trends for each rank can be reflected in the inferred information. These features enable more accurate inferred information. Furthermore, the type of inferred information (target period and rank) can be switched by user selection. Therefore, it is possible to select a type of inferred information according to the opponent (whether the opponent is a beginner, or which period's popularity the opponent's deck cards fall into, etc.) (in other words, the user's inference about the opponent can be reflected in the inferred information), thereby further improving the accuracy of the inferred information.

[0131] In the above embodiment, the first analytical model unit 23A and the second analytical model unit 23B of the game server 2 (or the processes of FIG. 12 executed by them, respectively) function as multiple types of processes of the present invention. Meanwhile, the model processing unit 35 of the user device 3 functions as a superiority / inferiority acquisition means and a superiority / inferiority providing means of the present invention by executing the procedure of FIG. 12. Specifically, the model processing unit 35 functions as a superiority / inferiority acquisition means by executing step S102 of FIG. 12, and as a superiority / inferiority providing means by executing step S104. Furthermore, the model processing unit 35 of the user device 3 functions as a filter means or a sort means of the present invention by executing the procedure of step S303 of FIG. 13.

[0132] Furthermore, the estimation model unit 24 of the game server 2 (or the process of FIG. 14 executed by it) functions as a predetermined process of the present invention. Meanwhile, the model processing unit 35 of the user device 3 functions as an information acquisition means and an information provision means of the present invention by executing the procedure of FIG. 14. Specifically, the model processing unit 35 functions as an information acquisition means by executing step S402 of FIG. 14, and as an information provision means by executing step S404.

[0133] The present invention is not limited to the above-described embodiments and may be embodied in embodiments with appropriate modifications or alterations. Furthermore, the present invention may be embodied in embodiments obtained by appropriately combining various technical means included in the above-described embodiments and the following modified embodiments. In the above-described embodiments, superiority / inferiority information and speculation information are provided to players in a card game provided as a video game. However, the present invention is not limited to such embodiments. For example, the superiority / inferiority information and speculation information may be provided to players in a game in real space or to viewers watching the game. Specifically, for example, in a game in real space, the superiority / inferiority information or speculation information may be provided by adding it to a video (viewed video) of the game, or may be provided via audio in the real space where the game is being played (which may include spectators as well as players).

[0134] In the above-described embodiment, each analytical model unit 23 (trained artificial intelligence model for analysis service), such as the first analytical model unit 23A, and each inference model unit 24 (trained artificial intelligence model for inference service), such as the first inference model unit 24A, are assigned different thoughts depending on the trained data, such as the trained data for analysis AD or the trained data for inference FD. However, the present invention is not limited to this embodiment. Each analytical model unit 23 and each inference model unit 24 may also be assigned different thoughts depending on, for example, the algorithm of the learning program (including the reward, i.e., the predetermined result, in reinforcement learning such as Q-learning) and the algorithm of the inference program.

[0135] In the above-described embodiment, the superiority / inferiority information is provided via the advice display field 60. However, the present invention is not limited to this embodiment. The superiority / inferiority information may be provided as appropriate. FIG. 15 is an explanatory diagram for explaining an example of a modified example of the display mode of the superiority / inferiority information. In the example of FIG. 15, two modified examples are shown: (1) the first modified example, and (2) the second modified example. As shown in FIG. 15 (1), in the first modified example, the probability values ​​of each analytical model section 23 are provided via the target card image 90, the first analysis result section 91, and the second analysis result section 92.

[0136] The target card image 90 is an image showing a candidate card object CO as an option. The target card image 90 may be the card object CO itself, or a thumbnail-like image that replaces it. The first analysis result section 91 and the second analysis result section 92 are sections that indicate the type of analytical model section 23 (type of analysis result), such as the first analytical model section 23A (e.g., offensive AI) or the second analytical model section 23B (e.g., defensive AI), respectively. The first analysis result section 91 and the second analysis result section 92 can be formed as appropriate, but in the example of FIG. 15, both are formed as rectangles surrounding the target card image 90, and are indicated by dashed lines and thick solid lines, respectively.

[0137] Furthermore, the first analysis result section 91 and the second analysis result section 92 are provided with numerical information sections 93 indicating probability values ​​(analysis results) from each analytical model section 23, such as "46.2." Furthermore, a sum information section 94 may also be provided. For example, in the first modified example of FIG. 15, three candidates are shown, and a sum information section 94 is added to the middle candidate. The sum information section 94 is a section that indicates the sum of probability values ​​from multiple types of artificial intelligence models for one candidate (option). Target card images 90 may be displayed for all candidates, but in the example of FIG. 15, only a portion, such as the top three, of the probability values ​​are displayed. Because probability values ​​from multiple types of analytical model sections 23 are output for all three candidates, the sum information section 94 may be displayed for all of them, but in the example of FIG. 15, only a portion of them (the middle target card image 90) is displayed. The display target of the sum information section 94 can be set as appropriate, but as an example, it is set to the candidate whose sum exceeds the highest probability value. For example, the center target card image 90 has numerical information sections 93 showing "25.2" and "32.0" in the first analysis result section 91 and second analysis result section 92, respectively, and the sum information section 94 shows the sum of these values, "57.2." This sum is higher than the probability values ​​of any of the other target card images 90. In such cases, the sum information section 94 is provided.

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

[0139] Further, 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 include various types of information, but in the example of FIG. 15, it includes information on the type of analytical model section 23 ("offensive AI" or "defensive AI"), such as the first analytical model section 23A (e.g., offensive AI) or the second analytical model section 23B (e.g., defensive AI), and information on the probability value ("46.1" or "32.0"). Although omitted in the example of FIG. 15, a summation 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, but 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 embodiment, the network system 1 includes the game server 2. However, the present invention is not limited to this embodiment. For example, in cases where an offline game that can be played without connecting to the network NT is provided, 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 the analytical model units 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 performed by an artificial intelligence model (a logical device similar to the analytical model unit 23, etc.) provided in the user device 3 and called by the model processing unit 35.

[0141] Alternatively, conversely, the game server 2 may perform all or part of the functions (various processes, etc.) of the user device 3. For example, learned data such as the analytical learned data AD or the inference learned data FD may be stored in the game server 2, and a program for an artificial intelligence model such as the analysis program AP or the inference program FP may be provided in the user device 3. Furthermore, data necessary for implementing the present invention may be appropriately distributed and recorded on the game server 2 and the user device 3, such as portions of various learned data or portions of programs for the artificial intelligence model. The game system of the present invention may be realized through 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, a combination of the user device 3 and the game server 2 (e.g., the network system 1), or the game server 2 alone (including when configured with multiple server devices), may function as the game system of the present invention. A program and a control method implemented in an apparatus such as the network system 1, the user device 3, or the game server 2 may function as the game program and control method of the present invention.

[0142] Various aspects of the present invention derived from the above-described embodiments and modifications will be described below. In the following description, corresponding components shown in the accompanying drawings will be written in parentheses to facilitate understanding of each aspect of the present invention, but the present invention is not limited to the illustrated forms.

[0143] The computer program of the present invention is configured to cause a computer (31) incorporated in a game system (3) that provides element information as information relating to at least one of a group of elements (CO) for each user in a game played by a plurality of users using the group of elements for each user to function as an information acquisition means (35) that acquires the speculated information using a predetermined process (24) that is set to obtain speculated information for inferring the content of at least one element in an imperfect information game of a type in which the content of at least one of the group of elements of other users provided as the game is not shared with the one user, and an information provision means (35) that provides the speculated information as the element information.

[0144] According to the present invention, elements that are not shared with a user, i.e., elements that are kept secret from a user, are inferred by a predetermined process, and the inferred result is provided as inferred information. Therefore, the inferred information can help infer the hidden elements in an incomplete information game. Note that the game of the present invention may be a game in real space and does not necessarily have to be provided by a game system. Similarly, the predetermined process does not necessarily have to be provided by a game system. For example, the predetermined process may be executed by a system (operated by a different system) different from the game system.

[0145] Various elements included in imperfect information games may be used as the element group. For example, when an imperfect information game such as mahjong or a card game is provided, elements such as other users' hand tiles or cards (cards in hand) may be used as the element group. Furthermore, like mahjong tiles in mahjong, each user's element group may be appropriately allocated from a fixed element group (mahjong tiles) that is common to all users. The same applies to card games such as sevens or poker. Alternatively, an imperfect information game may be played such that a portion of an element group assigned to each user is used, arbitrarily designated by the user. For example, in one aspect of the computer program of the present invention, each user's element group may be formed by two or more elements selected by the user as the denominator of the element group from a denominator element group assigned to the user.

[0146] An imperfect information game may be configured as appropriate. For example, in an imperfect information game, available elements may be fixed or may vary depending on appropriate conditions such as time. Also, changes in available elements may occur as appropriate; for example, the number or type of available elements may change. That is, unavailable elements may arise or new elements may be added depending on a predetermined time period or the like. When 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 imperfect information game may be provided so that available elements change at a predetermined time period. In this aspect, the change in available elements may include at least one of a change in number and a change in type. Similarly, in an aspect in which available elements change, the predetermined process may be configured to reflect the change in available elements in the speculation information. When available elements change depending on time or the like, it is likely that it will be more difficult to guess the hidden elements than when the available elements are fixed. Even when it is difficult to guess such hidden elements, the speculation information can assist in the guessing process. When changes in available elements are reflected in the inferred information, the accuracy of the inferred information can be improved compared to when similar changes are not reflected.

[0147] An imperfect information game may be configured as an appropriate game. For example, it may be configured as a cooperative game (including a game played in a team) or a competitive game (including not only one-on-one, but also one-on-many (including a game in which multiple users other than the player are independent opponents, such as in mahjong), and many-on-many). Similarly, it may be configured as a game simply played by one person. Furthermore, an imperfect information game may be a game played in real space or an electronic game. Therefore, the inferred information may be provided as appropriate. For example, in a game played in real space, the inferred information may be provided by adding it to a video of the game (such as displayed on a monitor or a wearable device), or may be provided via audio in the real space where the game is played. Similarly, in an electronic game, the inferred information may be displayed on a game screen for the game, or may be provided via audio. Furthermore, an electronic game may be configured as appropriate, and may be configured to be provided via an appropriate object on a display device that displays a game screen including various objects, for example. 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 predetermined rules via a plurality of card objects (CO) on a display device (MO) that displays the plurality of card objects (CO) functioning as element groups 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 via the game screen.

[0148] Furthermore, in a case where a card game is provided as an incomplete information game, changes in usable elements (e.g., whether or not an element can be used due to a rule change) may or may not be reflected in the speculation information as described above. However, if they are reflected in the speculation information, such reflection may be implemented appropriately. For example, after a speculation result is obtained, the unusable element may be simply removed from the speculation result, thereby reflecting the unusable element in the speculation information. Alternatively, after the unusable element is removed, the speculation result may be adjusted again (e.g., the effect of the unusable element being removed may be reflected in the speculation information). In either case, the unusable element may be reflected by excluding the unusable card object from the speculation information. Specifically, in a case where a card game is provided as an incomplete information game, changes to usable card objects in the card game may include a change that makes a specific card object unusable due to the rule change, and the predetermined process may be set to reflect the unusable card object in the speculation information by removing the specific card object.

[0149] A 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 satisfy predetermined granting conditions, with some card objects owned by each user, 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 play, or only an appropriate portion, such as card objects selected by each user, may be used in play. Specifically, for example, in a mode in which the card game is provided as an incomplete information game, the card game may be configured to grant card objects (CO) to users who satisfy predetermined granting conditions, and the denominator element group may be composed of the card objects (CO) granted to each user. In this case, compared to when the available elements are fixed, the candidates for the hidden elements change with each play, making it more difficult to guess. Even when guessing such hidden elements is difficult, guess information can help with the guess.

[0150] When a card game is provided as an incomplete information game, the card game may be configured to appropriately use multiple card objects (element groups). An appropriate portion of the multiple card objects may be the subject of speculation information. For example, in a mode in which the card game is provided as an incomplete information game, the card game is played using multiple card placement areas (CP) in which card objects to be used in a match with the other user are placed, a hand area (57) in which hand cards (CO5) as candidate card objects to be placed in each card placement area are placed, and a deck area (55) in which deck cards (CO6) as candidate card objects to be added to the hand are placed, and the predetermined processing may be configured to obtain, as the speculation information, the content of at least one card object in the multiple card placement areas, the hand, and the deck among the multiple card objects of the other user.

[0151] The predetermined process may be configured as appropriate as long as it can obtain inferred information. For example, each process may be configured to output inferred 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 to obtain inferred information. Furthermore, the predetermined process may be configured as a single process that obtains one type of inferred information, or as multiple processes that obtain multiple pieces of inferred information for one secret element. For example, in one aspect of the computer program of the present invention, the predetermined process may be configured as a trained artificial intelligence model generated to obtain the inferred information by machine learning a predetermined training dataset. In this aspect, the trained artificial intelligence model may include multiple trained artificial intelligence models (24A, 24B) each generated by machine learning a plurality of training datasets (FDs) with different contents. In card games, trends may occur in the types of card objects (element groups) used. When trends occur, artificial intelligence models that reflect those trends are more likely to output more accurate inferred information. Trends often occur periodically. Similarly, there are often differences in the types of card objects incorporated into the group of card objects used and the methods of use depending on the rank (user skill). For this reason, by training a learning dataset including performance data for a predetermined period, it is possible to reflect trends for each period in the estimated information. Alternatively, by training a learning dataset including performance data for each rank, it is possible to reflect usage trends for each rank in the estimated information. These make it possible to achieve more accurate estimations.

[0152] When the trained artificial intelligence model includes multiple trained artificial intelligence models, multiple pieces of inferred information obtained by the multiple trained artificial intelligence models may be provided, or a portion of the multiple pieces of inferred information may be provided. When a portion of the multiple pieces of inferred information is provided, the portion may be appropriately identified, for example, based on the playing situation, or based on evaluations over a certain period of time (such as hit rates or user evaluations), or may be identified based on a user's designation. For example, when the trained artificial intelligence model includes multiple trained artificial intelligence models, the inferred information may include multiple pieces of inferred information obtained using the multiple trained artificial intelligence models, and the information providing means may provide the single user with inferred information corresponding to a trained artificial intelligence model selected by the single user from the multiple pieces of inferred information.

[0153] On the other hand, the game system of the present invention is a game system incorporating a computer that provides element information as information relating to at least one of the element groups (CO) for each user in a game played by multiple users using the element groups for each user, and the computer functions as an information acquisition means (35) that acquires the guessed information using a predetermined process (24) set to obtain guessed information for guessing the content of at least one element in an imperfect information game provided as the game, in which the content of at least one of the element groups of other users is not shared with a single user, and as an information provision means (35) that provides the guessed information as the element information.

[0154] Furthermore, the control method of the present invention is a method for causing a computer (31) incorporated in a game system (3) that provides element information as information relating to at least one of a group of elements (CO) for each user in a game played by a plurality of users using the group of elements for each user to execute the following steps in an imperfect information game of a type in which the content of at least one of the group of elements of other users provided as the game is not shared with a single user: acquiring the speculated information using a predetermined process (24) that is set to obtain speculated information for speculating the content of the at least one element; and providing the speculated information as the element information.

[0155] (Reference example) Non-Patent Document 1 discloses a technology that adds information such as "winning rate," "potential moves," and "reading lines" that are simulated for each move by AI (artificial intelligence) to a live broadcast screen that captures a shot of the shogi board from above during a game. However, this information (simulation results) is merely the result of a simulation based on one type of AI.

[0156] In general, AI tends to have a way of thinking (or perhaps even a personality) influenced by the training datasets that served as the basis for machine learning. For example, differences in the content of the training dataset or the machine learning learning methods (algorithms) often lead to AIs developing unique ways of thinking. For this reason, if the same move in the shogi game described in Non-Patent Document 1 were simulated by multiple AIs, the simulation results of one AI may differ from those of another. If only simulation results based on one AI are provided, the simulation results may be biased, or the simulation may be performed for a purpose other than the intended purpose of the move. As a result, the reliability of the simulation results may decrease. Even if this is not the case, there may be a wide demand for AIs with different ways of thinking, in other words, simulation results from various perspectives. Simulation results may be calculated not only by AI but also by various other logics, and similar situations may arise in such cases.

[0157] Therefore, an object of the present invention is to provide a computer program or the like that can provide information on the merits and demerits of each option from various perspectives in a game that includes a selection opportunity in which some of a plurality of options must be selected.

[0158] The computer program of the reference example is configured to cause 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 of each option in a game including a selection opportunity to select some of a plurality of options before the user selects one of the options, to function as a superiority / inferiority acquisition means (35) that acquires multiple pieces of superiority / inferiority information for each option of the at least one option using multiple types of processing (23A, 23B) that outputs the superiority / inferiority information for each of the at least one option, and a superiority / inferiority provision means (35) that provides the multiple pieces of superiority / inferiority information for each option before the user selects the some of the options.

[0159] According to the reference example, multiple pieces of superiority / inferiority information are output for each option by multiple types of processing, and this information is provided as superiority / inferiority information before some of the options are selected. Therefore, in a game that includes a selection opportunity in which some of the options must be selected, superiority / inferiority information can be provided for each option from various perspectives. Note that the game in the reference example may be a game that takes place in real space and does not necessarily need to be provided by a game system.

[0160] The superiority / inferiority information may be provided in an appropriate game that includes a choice opportunity. For example, the superiority / inferiority information may be provided in a cooperative game that includes a choice opportunity (including a game played in a team) or a competitive game (including one-on-one, one-on-many (including a game in which multiple users other than the player are independent opponents, such as in mahjong), and many-on-many). Similarly, the superiority / inferiority information may be provided in a game that is simply played alone. Furthermore, various influences that each option has on victory in each game may be output as the superiority / inferiority information. For example, in a game that has a concept of winning, the influence on victory may be output as the superiority / inferiority information. Alternatively, the influence on a part of the progress, such as a specific development provided in the game (including a specific mission and the acquisition of bonus points, etc.), may be output as the superiority / inferiority information. Specifically, for example, in one aspect of the computer program of the reference example, a competitive game in which the user competes against an opponent is provided as the game, and the multiple types of processing may be configured to output, as the multiple pieces of superiority / inferiority information, all information regarding the superiority / inferiority of the influence that each option has on victory in the competitive game. In this case, information regarding the influence on victory in a competitive game can be provided for each option from various perspectives.

[0161] The superiority / inferiority information may be any suitable information regarding superiority / inferiority. For example, it may be symbolic information such as A, B, or C that distinguishes the degree of superiority / inferiority, or information that intuitively expresses superiority / inferiority, such as "looks good" or "looks bad." Alternatively, the superiority / inferiority information may be information that quantifies superiority / inferiority. When superiority / inferiority is quantified, the numerical value may indicate the likelihood of progressing advantageously (least likely to be at a disadvantage) within a predetermined range (including any suitable range, such as a predetermined number of selection opportunities, a predetermined time, or a predetermined development), or may of course indicate the likelihood of ultimately winning the battle. For example, in an aspect in which a competitive game is provided, the multiple types of processing may be configured to output, as the multiple pieces of superiority / inferiority information, any numerical information that indicates the likelihood of progressing advantageously in the competitive game through the magnitude of the numerical value.

[0162] A competitive game may be configured as appropriate. For example, a competitive game may be configured so that a user and an opponent make choices simultaneously (in parallel), or so that turns including selection opportunities are given alternately. Even when turns are given alternately, the choice of the player outside of the turn (which may function as a selection opportunity in the reference example) may not be completely eliminated in each turn, for example, by requiring the opponent to make a choice in response to a choice made in the user's turn. Furthermore, when turns are given alternately, the turn change may be automatically performed according to time, the number of instructions, etc., or may be performed as one of the choices in each turn. When a turn changes due to a choice, the choice may function as a target for superiority / inferiority information, or may be excluded from the target for superiority / inferiority information. Furthermore, each turn may be configured as appropriate, for example, it may include only one selection opportunity, or it may include multiple selection opportunities. The multiple selection opportunities may be distinguished as appropriate 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. Furthermore, the end of each phase may be automatic or optional. If the end of each phase is optional, the choice may or may not be subject to superiority information.

[0163] For example, in a mode in which a competitive game is provided, the competitive game may be configured such that turns including the selection opportunities are alternately provided between 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 mode, each turn may include multiple phases each providing multiple selection opportunities, and the selection opportunities in each phase may be configured to provide two or more options as the plurality of options, each including different options between the selection opportunities in each phase, and the two or more options in each selection opportunity may each include an option corresponding to a phase end instruction for ending the phase. When a turn change functions as one of the options, the impact of a selection made at a selection opportunity on progress tends to be more complex. This is likely to become even more complex when each turn includes multiple selection opportunities and each of these selection opportunities ends selectively. When options corresponding to a turn end instruction or the like are the subject of superiority / inferiority information, multiple pieces of superiority / inferiority information can be provided from various perspectives regarding complex choices.

[0164] A game including a selection opportunity may be a game played in real space or an electronic game. Therefore, superiority / inferiority information may be provided as appropriate. For example, in a game played in real space, superiority / inferiority information may be provided by adding it to a video of the game (such as a display on a monitor or a wearable device) or may be provided via audio in the real space where the game is played. Similarly, in an electronic game, superiority / inferiority information may be displayed on a game screen for the game or may be provided via audio. Furthermore, electronic games may be configured as appropriate, for example, to be provided via appropriate objects on a display device that displays a game screen including various objects. Specifically, in an aspect in which a competitive game is provided, the competitive game may be provided as a game in which a player competes against an opponent according to predetermined rules via a plurality of objects (CO) on a display device (MO) that displays the plurality of objects (CO).

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

[0166] Specifically, for example, in an aspect in which a battle-type game is provided, the battle-type game is provided as a card game in which a plurality of card objects (CO) are used as the plurality of objects, and the card game is played using a plurality of card placement areas (CP) in which the card objects to be used in the battle with the opponent are placed, a hand area (57) in which hand cards (CO5) as candidate card objects to be placed in each card placement area are placed, and a deck area (55) in which deck cards (CO6) as candidate card objects to be added to the hand are placed, 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 adding cards from the deck to the hand. In this case, it is possible to provide a plurality of pieces of superiority / inferiority information from various perspectives in a game that requires complex choices including many options (including further options derived from each option).

[0167] The multiple types of processes may be configured as appropriate as long as they can output multiple pieces of superiority / inferiority information. For example, each process 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 multiple types of processes 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 multiple types of processes may be configured as a trained artificial intelligence model generated by machine learning a predetermined training dataset into a pre-trained model to output superiority / inferiority information regarding a predetermined result in the game as the superiority / inferiority information. Furthermore, in this aspect, all of the multiple types of processes may be configured as the trained artificial intelligence model and configured to output the multiple pieces of superiority / inferiority information based on differences in at least one of the predetermined result, the training dataset, and the algorithm of the pre-trained model used to train the training dataset.

[0168] Multiple pieces of superiority / inferiority information may be provided as appropriate. For example, the superiority / inferiority information may be provided in a predetermined order for each option, or if quantified, may be provided in descending order, or in other numerical order. The order in which each piece of superiority / inferiority information is provided may be fixed or variable. For example, the multiple pieces of superiority / inferiority information may be provided in an order specified by a user. Alternatively, the multiple pieces of superiority / inferiority information may be provided after being limited to a portion thereof based on predetermined filter conditions. The filter conditions may be fixed or variable. For example, the filter conditions may be specified by a user. Specifically, in one aspect of the computer program of the reference example, the superiority / inferiority providing means may include at least one of a filter means (35) that limits the multiple pieces of superiority / inferiority information to a portion thereof that satisfies predetermined filter conditions, and a sorting means (35) that rearranges the multiple pieces of superiority / inferiority information according to predetermined sort conditions. In this case, the multiple pieces of superiority / inferiority information are provided after being limited to a portion thereof or rearranged as appropriate based on the filter conditions, etc. This improves the convenience of accessing specific pieces of superiority / inferiority information through the filter conditions, etc.

[0169] On the other hand, the game system of the 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 of each option for at least one of a plurality of options in a game including a selection opportunity in which some of the options must be selected, before the user selects one of the options, and the computer functions as a superiority / inferiority acquisition means (35) that acquires multiple pieces of superiority / inferiority information for each option of the at least one option using multiple types of processing that outputs the superiority / inferiority information for each of the at least one option, and a superiority / inferiority providing means (35) that provides the multiple pieces of superiority / inferiority information for each option before the user selects the one of the options.

[0170] Furthermore, the control method of the reference example is a method in which 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 of each option in a game including a selection opportunity in which some of a plurality of options must be selected, for at least one of the plurality of options before the user selects one of the options, executes the following steps: obtaining multiple pieces of superiority / inferiority information for each option of the at least one option using multiple types of processing that outputs the superiority / inferiority information for each of the at least one option; and providing the multiple pieces of superiority / inferiority information for each option before the user selects the portion of the options. [Explanation of symbols]

[0171] 3. User Device (Game System) 31 Control unit (computer) 35 Model processing unit (superiority acquisition means, superiority provision means, filter means, sort means, information acquisition means, information provision 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 storage area (card placement area) MO display device CO5 Hand CO6 Deck PG2 Game Program (Computer Program)

Claims

1. A computer incorporated in a game system that provides element information as information regarding at least one element of a group of elements for each user in a game played by a plurality of users by placing each element in one of a plurality of placement locations on a game field using a group of elements for each user, In an incomplete information game provided as the game, in which the content of at least one of a group of elements of other users is not shared with a single user, an information acquisition means for acquiring inferred information by utilizing a predetermined process set to obtain the inferred information for inferring the content of the at least one element based on information on the game field including information on individual placement locations of each element as information on placement locations of each element; an information providing means for providing the inferred information as the element information; A computer program configured to function as

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

3. The computer program according to claim 2 , wherein the imperfect information game is provided such that available elements change over time.

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

5. The computer program product according to claim 3 , wherein the predetermined process is configured to reflect changes in the available elements in the estimated information.

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

7. The change in the card objects that can be used in the card game includes a change that makes a specific card object unusable in accordance with the change in the rules, 7. The computer program according to claim 6, wherein the predetermined process is set so that the unusability of the specific card object is reflected in the guess information by excluding the specific card object.

8. the card game is configured to award a card object to a user who satisfies a predetermined awarding condition; The computer program according to claim 6 , wherein the denominator element group is constituted by card objects assigned to each user.

9. the card game is played using a plurality of card placement areas in which card objects to be used in a match against the other user are to be placed, a hand area in which a hand of cards as candidate card objects to be placed in each card placement area is to be placed, and a deck area in which a deck of cards as candidate card objects to be added to the hand is to be placed; 9. The computer program according to claim 8, wherein the predetermined processing is set so that the content of at least one card object of the other user's card placement areas, hand, and deck is obtained as the speculated information.

10. the display device displays a game screen including the plurality of card objects; 10. The computer program according to claim 6, 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 processing is configured as a trained artificial intelligence model generated so as to obtain the inferred information by machine learning a predetermined training dataset.

12. The computer program according to claim 11, wherein the trained artificial intelligence model includes a plurality of trained artificial intelligence models each generated by machine learning a plurality of training data sets having different contents.

13. the inferred information includes a plurality of pieces of inferred information obtained by using the plurality of trained artificial intelligence models, The computer program according to claim 12 , wherein the information providing means provides the one user with inferred information corresponding to a trained artificial intelligence model selected by the one user from the plurality of inferred information.

14. 1. A game system incorporating a computer that provides element information as information relating to at least one of a group of elements for each user in a game played by a plurality of users by placing each element in one of a plurality of placement locations on a game field, the game system comprising: The computer In an incomplete information game provided as the game, in which the content of at least one of a group of elements of other users is not shared with a single user, an information acquisition means for acquiring inferred information by utilizing a predetermined process set to obtain the inferred information for inferring the content of the at least one element based on information on the game field including information on individual placement locations of each element as information on placement locations of each element; an information providing means for providing the inferred information as the element information; It functions as a game system.

15. A computer incorporated in a game system that provides element information as information regarding at least one of a group of elements for each user in a game played by a plurality of users by placing each element at one of a plurality of placement locations on a game field using a group of elements for each user, In an incomplete information game provided as the game, in which the content of at least one of a group of elements of other users is not shared with a single user, a step of acquiring inferred information using a predetermined process set to obtain inferred information for inferring the content of the at least one element based on information on the game field including information on individual placement locations of each element as information on placement locations of each element; providing the inferred information as the element information; A control method for executing the above.

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