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

Abstract

To provide a computer program capable of acquiring more appropriate influence information about a series of selections in a series of selection opportunities.SOLUTION: A computer program PG2 causes a control unit 21 (computer) incorporated in a game server 2 (game system) for providing a card game including progress according to a selection result in a series of selection opportunities to function as the following means. That is, the computer program PG2 causes the control unit 21 to function as: means for acquiring game information necessary for obtaining a probability value regarding an influence of each selection set on winning of the card game, the selection set being a combination of a series of selections in a series of selection opportunities as one selection set; and means for acquiring a probability value regarding each selection set by using an analytical model unit 23 that outputs a probability value on the basis of the game information.SELECTED DRAWING: Figure 6

Description

[Technical Field]

[0001] The present invention relates to a computer program that is applied to a computer incorporated in a game system that provides a game that progresses according to the results of selections made in a series of selection opportunities. [Background technology]

[0002] There are game systems that provide a game that progresses according to the results of selections made in a series of selection opportunities. For example, a game system that provides a card game via a game screen that includes card objects is known (see, for example, Patent Document 1). In addition, Patent Document 2 is a prior art document related to the present invention. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 2018-175636 [Patent Document 2] Patent Publication No. 2021-159187 Summary of the Invention [Problem to be solved by the invention]

[0004] The card game of Patent Document 1 includes various selection opportunities regarding card objects according to predetermined rules, and progresses according to the selection results at each selection opportunity. The rules of the card game may be changed as appropriate. Furthermore, various card games may be provided according to different rules. The card game of Patent Document 2 is an example of a card game provided according to different rules.

[0005] Card games such as those described in Patent Document 1 or Patent Document 2 are often provided as competitive games. Competitive games often include single-player computer battles (hereinafter sometimes referred to as "COM battles"). Meanwhile, card games such as those described in Patent Document 1 or Patent Document 2 include a series of selection opportunities that are provided as a set. For example, the card game described in Patent Document 1 always requires a series of selections, such as which card to place on the duel field from the hand, the specific position where that card should be placed, whether or not the card's effect will be activated, and where that effect will be activated. Therefore, a series of selection opportunities arise for these series of selections. The same is true for the card game described in Patent Document 2.

[0006] However, this type of series of selection opportunities is likely to lead to complicated game developments. For example, it is necessary to consider situations that arise from card combinations. It is also necessary to select factors to be considered based on the effects of card objects, and in the case of computer battles, to devise calculation formulas. Deep knowledge of the game as a whole is also required. Furthermore, card games such as those described in Patent Document 1 or Patent Document 2 generally have a huge variety of cards, and new cards are often added continuously.

[0007] Furthermore, a series of choice opportunities is often presented in such a way that options derived from the first choice unfold in sequence. In this type of series of choice opportunities, considering the first choice alone may not yield an appropriate result. For example, if a card effect (progression) occurs after the final choice in the series, without considering the appropriateness of the choice in the final choice opportunity, it may be impossible to make an appropriate decision by considering only the appropriateness of each option in the first choice opportunity. In this case, a computer-based match requires consideration of not only the candidate options in the choice opportunity faced, but also the candidate options in the final choice opportunity in the series. For this reason, games that include a series of choice opportunities need computer-based matchups that enable more appropriate decisions. This need is not limited to computer-based matchups; similar needs exist when computer-based decisions are required, such as computer advice. Furthermore, similar needs exist in games other than card games as long as they include a series of choice opportunities.

[0008] Therefore, an object of the present invention is to provide a computer program or the like that can acquire more appropriate influence information for a series of choices in a series of choice opportunities. [Means for solving the problem]

[0009] The computer program of the present invention is configured to cause a computer incorporated in a game system that provides a game that progresses according to the results of selections made in a series of selection opportunities to function as: information acquisition means for acquiring game information indicating the state of the game, which is necessary to obtain influence information regarding the influence that each selection set has on the game, with a combination of a series of selections made in the series of selection opportunities being treated as one selection set; and influence acquisition means for acquiring the influence information regarding each selection set by utilizing a predetermined process that outputs the influence information based on the game information.

[0010] On the other hand, the game system of the present invention is a game system that provides a game that progresses according to the results of selections made in a series of selection opportunities, and is equipped with an information acquisition means that acquires game information indicating the state of the game necessary to obtain impact information regarding the impact that each selection set has on the game, with a combination of selections made in the series of selection opportunities being treated as one selection set, and an impact acquisition means that acquires the impact information regarding each selection set by utilizing a predetermined process that outputs the impact information based on the game information.

[0011] Furthermore, the control method of the present invention causes a computer incorporated in a game system that provides a game that progresses according to the results of selections made in a series of selection opportunities to execute an information acquisition procedure that acquires game information indicating the state of the game necessary to obtain influence information regarding the influence that each selection set has on the game, with a combination of a series of selections made in the series of selection opportunities being treated as one selection set, and an influence acquisition procedure that acquires the influence information regarding each selection set using a predetermined process that outputs the influence information based on the game information. [Brief explanation of the drawings]

[0012] [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 an explanatory diagram for explaining an example of a series of selection opportunities. [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 an explanatory diagram illustrating an example of a chain. [Figure 9] 10 is a flowchart showing an example of a procedure for selection execution processing. [Figure 10] 10 is a flowchart showing an example of a procedure for a selection redetermining process. [Figure 11] 10 is a flowchart showing an example of a procedure for a battle AI process. [Figure 12] A diagram showing an overview of the UDI system configuration. [Figure 13] A table showing the win rate after 2000 matches against computer games. [Figure 14] A table showing some of the input features. [Figure 15] A table showing the results of 4000 matches against the COM at the end of the training period. DETAILED DESCRIPTION OF THE INVENTION

[0013] (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. The game server 2 is a computer device that implements a computer program according to an embodiment of the present invention. The game server 2 functions as a game system according to an embodiment of the present invention in the network system 1.

[0014] 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 may be a smartphone or a tablet terminal equipped with a communication function. The user device 3 may be a PC (short for personal computer), or may be a personal or home game machine provided as a so-called consumer game machine. Furthermore, the user device 3 may be an arcade game machine for commercial use. The following describes, as an example, a case where a smartphone is used as the user device 3.

[0015] The user device 3 functions as a game machine by implementing predetermined software (applications). The user device 3 provides a game that progresses according to the results of selections made in a series of selection opportunities. The user device 3 may provide appropriate games, such as role-playing games, simulation games, or action games, as a game machine. As an example, the user device 3 provides a competitive game (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 where multiple users other than the user are independent opponents, such as in mahjong) or a many-on-many format. The following describes, as an example, a competitive game played in a one-on-one format.

[0016] Competitive games may be provided as either perfect information games such as shogi or chess (both of which use pieces as objects), or imperfect information games. In contrast to perfect information games in which the contents of all of the various elements necessary for making a selection in a selection opportunity are shared with the user, imperfect information games are games 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. Below, we will explain, as an example, the case where a card game classified as an imperfect information game is provided as a competitive game.

[0017] 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. The analysis service is a service for assisting the progress of the game. Specifically, the analysis service is a service for analyzing the impact on the card game of choices made to progress the game. The analysis service will be described in detail later.

[0018] The game server 2 may also provide various other services to the user device 3, such as a distribution service, a matching service, or a relay service. The distribution service is a service that distributes computer programs and various data necessary for playing a game on the user device 3. The matching service is a service that matches users who want to cooperate or compete in a game. The relay service is a service that relays game information that should be shared between user devices 3.

[0019] 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.

[0020] (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).

[0021] The memory unit 22 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 array. The memory unit 22 may be configured to store all data on one storage unit, or may be configured to store data in a distributed manner across multiple storage 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. In the example of FIG. 2, an analysis program AP is shown as an example.

[0022] The analysis program AP is a computer program that causes the control unit 21 to execute various processes for realizing an analysis service. The analysis program AP may be configured as appropriate, and may be configured, for example, to cause the control unit 21 to execute a process for outputting an analysis result based on a predetermined logic (including predetermined rules such as rules). In the example of FIG. 2, the analysis program AP is configured to cause the control unit 21 (e.g., a GPU) to function as an artificial intelligence model that outputs an analysis result. Specifically, the analysis program AP is configured as an inference program that incorporates analytical trained data (parameters) AD that are output by having a training program (for analysis) learn a training dataset (for analysis).

[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. In the example of FIG. 2, play data PD and analytical learned data AD are shown as examples. The play data PD describes information about each user's past playing history. The play data PD may include other information necessary for managing each user, such as various 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 appropriately, and are managed in 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 grant resulting from game progress). The card objects granted to each user (hereinafter sometimes referred to as "owned cards") are managed in the play data PD so as to be associated with each user.

[0024] The control unit 21 can be provided with various logical devices by combining the hardware resources of the control unit 21 with the server program PG1 as a software resource. In the example of Figure 2, an analytical model unit 23 and a COM battle realization unit 25 are shown as examples.

[0025] The analytical model unit 23 is a logical device that functions as an artificial intelligence model. The analytical model unit 23 functions as an artificial intelligence model that outputs analysis results based on the analytical program AP. The analytical model unit 23 (artificial intelligence model) is realized, for example, by a combination of analytical trained data AD and the analytical program AP (the analytical program AP executed by incorporating the analytical trained data AD), and performs various processes to realize the analytical service. This type of artificial intelligence model can form different thoughts depending on various factors. For example, this type of artificial intelligence model tends to have different thoughts depending on at least one difference in the content of the training dataset, the algorithm of the training program (in other words, the content of the analytical trained data AD), and the algorithm of the inference program. The analytical model unit 23 performs various processes related to the analytical service in accordance with the analytical program. This process includes, for example, outputting impact information regarding the impact of each selection on the game based on a game situation indicating the game situation.

[0026] The COM match realization unit 25 is a logical device that executes various processes for realizing a computer match (COM match) in a card game. Various modes may be provided for a card game, and an AI match mode in which a player competes against an AI is provided as an example. When the AI ​​match mode is selected in the card game, the COM match realization unit 25 executes various processes for realizing the match. For example, in the AI ​​match mode, the COM match realization unit 25 executes a process for determining each selection in a series of selection opportunities based on the analysis results output by the analytical model unit 23. As such processes, the COM match realization unit 25 executes, for example, a selection execution process and a selection re-determination process. The procedures for the selection execution process and the selection re-determination process will be described in detail below.

[0027] In addition, the control unit 21 may be provided with a service management unit as a logical device 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.

[0028] 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).

[0029] 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 from the game server 2 as appropriate, in which case the game data GD may include the analysis program AP.

[0030] The game data GD may include various data (including various tables) necessary for playing the game, such as image data, audio data, and card data. Image data is data for displaying various images for the game. Audio data is data for playing various sounds (including background music such as music). Card data is data for defining each card object. In the example of FIG. 2, play data PD is shown as an example of such various data. The play data PD is provided and saved from the game server 2 as needed. Furthermore, when an analysis program AP is provided to the user device 3 from the game server 2, the game data GD may include analytical learned data AD.

[0031] The control unit 31 is provided with a progress control unit 33 and a data management unit 34 as logical devices realized by combining the hardware resources of the control unit 31 and the game program PG2 as software resources. 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 in the play data PD as an owned card.

[0032] 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 service management unit, and processes for reflecting the opponent's play on the player's own progress during a match, and vice versa. The progress control unit 33 also executes processes for providing selection opportunities during each turn or phase, which will be described later, and for switching between turns and phases.

[0033] The processing executed by the progress control unit 33 includes processing for realizing the AI ​​battle mode. Therefore, the progress control unit 33 also executes processing in cooperation with the COM battle realization unit 25 of the game server 2. For example, the progress control unit 33 executes processing for actually executing each selection determined by the COM battle realization unit 25 in the AI ​​battle mode. As an example of such processing, the progress control unit 33 executes a selection execution processing and a selection re-determination processing in cooperation with the COM battle realization unit 25.

[0034] The progress control unit 33 also generates a situation log SL that records game information necessary for analysis by the analytical model unit 23, 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 game situation as game information. For example, the situation log SL (data) includes various information (which may be limited to an appropriate portion of the information) for determining the status 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.

[0035] The user device 3 is provided with appropriate output devices and input devices. In the example of FIG. 2, a monitor MO, a speaker SK, and a touch sensor TS are shown as examples of 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.

[0036] (Game Overview) Next, an overview of the card game will be described with reference to Figures 3 and 4. The card game may be configured as an appropriate game. As an example, the card game is configured as a video game played via a game screen including various areas where each card object should be placed. 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.

[0037] 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 each other's deck cards. Various card objects may be arranged (displayed) on the battle screen 50, but in the example of FIG. 3, for ease of explanation, regardless of type or content, card objects CO facing up (the orientation in which the content of the card object can be viewed by the user) are shown with a dotted pattern, and card objects facing down (the orientation in which the content of the card object cannot be viewed and is kept secret) are shown with a right-side diagonal line. The predetermined number constituting a deck card may be fixed or variable, and may not match the number of deck cards of the opponent.

[0038] 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. In the example of Fig. 3, the user area 51 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, an excluded area EA, and a point display area LR.

[0039] The field area 54 is an area where a plurality of card places CP (only some of the symbols are shown) are formed. Each card place CP is a location (area) where a card object CO from the deck of cards is placed to progress the game. Various card objects CO with various roles (purposes) and effects may be prepared for a card game. For example, card objects CO 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") may be prepared. When a card object CO is placed in each card place CP, the card object CO actually performs its role in the battle.

[0040] For example, a monster card CO1 is assigned a role such as attacking or defending against the opponent or the opponent's monster cards CO1. Monster cards CO1 can also be classified into various types. As an example, monster cards CO1 include two types: normal monster cards CO1 and extra monster cards CO1. Normal monster cards CO1 are monster cards CO1 that can be placed from the hand placement area 57 to the field area 54.

[0041] On the other hand, the 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 the extra monster card CO1 is normally 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, the monster card CO1 may have the role of summoning (calling) other monster cards CO1 to the field area 54, etc. The monster card CO1 placed in the card area CP is used to actually impart roles such as attack, defense, or summoning to the progress of the game.

[0042] Similarly, magic cards CO2, trap cards CO3, and special cards CO4 are card objects CO that can be placed from the hand placement area 57 to the field area 54. These are placed in the card area CP and are used to activate the effects indicated by each card object CO.

[0043] The multiple card placement areas CP may be arranged appropriately in the field area 54. In the example of FIG. 3, the multiple card placement areas CP 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. As an example, each card placement area CP is limited in the type of card object CO that can be placed therein. The front row 54A and the back row 54B may be used appropriately, and as an example, they may be used according to the type of card object CO that can be placed therein.

[0044] Specifically, the front row 54A is used as card spaces CP where monster cards CO1, one of the card object CO types, should be placed. However, of the six card spaces CP that make up the front row 54A, the leftmost card space CP (which may be formed as desired, but in the example of FIG. 3, it is formed slightly smaller than the other card spaces CP) is the only exception. The leftmost card space CP is used as a card space CP where special cards should be placed. In other words, the placement of card objects CO such as magic cards CO2 or trap cards CO3 in the front row 54A is restricted, the placement of special cards CO4 in any space other than the leftmost card space CP in the front row 54A is restricted, and the placement of monster cards CO1 in the leftmost card space CP is further restricted.

[0045] 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 on 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.

[0046] In the field area 54, card objects CO can be placed as appropriate in accordance with the user's instructions, subject to the restrictions of each card placement area CP. 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, such as vertically or horizontally, in the card placement area CP, and the placement direction, such as vertically, can be used appropriately. In the example of FIG. 3, the monster card CO1 is placed horizontally when used in defense and vertically when used in attack.

[0047] The hand placement area 57 is an area where card objects CO (hereinafter, may be referred to as hand CO5) from the deck cards that are virtually at hand should be placed (displayed). The hand CO5 is a card object CO that is a candidate to be placed in the field area 54 (each card placement area CP). An appropriate number of hand cards CO5 may be placed in the hand placement area 57. In the example of FIG. 3, five hand cards CO5 are placed in the hand placement area 57.

[0048] 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 hand placement area 57) should be placed. However, the main deck placement area 55 and the extra deck placement area 56 have different uses.

[0049] Specifically, the main deck placement area 55 is an area where the remaining deck cards, including card objects CO (hereinafter sometimes referred to as the deck CO6) that are candidates for addition to the hand CO5, should be placed. The card objects CO from 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 extra deck cards CO7) consisting of 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 extra deck cards CO7. In other words, the remaining deck cards are divided into the deck CO6 and the extra deck cards CO7, and are placed in the main deck placement area 55 and the extra deck placement area 56, respectively.

[0050] 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 a predetermined effect, 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. In the example of FIG. 3, the graveyard area 58 is configured to omit displaying card objects CO.

[0051] The exclusion area EA is an area for placing card objects CO that have been removed from the game. When a predetermined exclusion condition is met, the card object CO is removed from the game and placed in the exclusion area EA. The exclusion area EA may be configured to display the card object CO placed there, but in the example of FIG. 3, it is configured to omit display of the card object CO, similar to the graveyard area 58.

[0052] The point display area LR is an area for displaying the user's life points. In a card game, appropriate life points may be set for both the user and the opponent. As an example, 8000 life points are set for each player, and in the example of FIG. 3, "8000" (initial value) representing this value is displayed as the remaining balance in the point display area LR. The victory or defeat 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 an attack. 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 may be configured as appropriate. In the example of FIG. 3, two card spaces CP are provided in the common area 52. The user's card object CO and the opponent's card object CO are appropriately placed in each card space CP. As an example, placement in each card space CP is limited to extra monster cards CO1. In other words, an extra monster card CO1 summoned from an extra deck card CO7 is first placed in each card space CP in the common area 52, and is only allowed to be placed in the field area 54 when certain field conditions are met. Note that placement in each card space CP in the common area 52 may be allowed without restriction, regardless of the type of card object CO.

[0054] The opponent area 53 is an area reserved for the opponent. The opponent area 53 plays the same role as the user area 51 for the opponent. 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, an excluded area EA, and a point display area LR. Since their roles are the same as those of the user area 51, their explanation 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 (including a computer). The game proceeds in a so-called turn-based format, in which the user and the opponent (hereinafter, sometimes referred to as "players" when no distinction is made between the two) alternate (in order) throughout 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 to be 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 by the player during the Battle Phase. Furthermore, the types of phases provided in each turn may vary as appropriate, such as skipping the Draw Phase, Battle Phase, and Second Main Phase during the first turn. 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 decrease to a predetermined value (e.g., 0) through combat.

[0058] (Analysis services) Next, with reference to FIG. 5, the analysis service will be described in detail. As described above, the analysis service is a service that analyzes the impact of each choice on the card game. The analysis service may provide various information regarding the impact of each choice (hereinafter, sometimes referred to as impact information) as the analysis result. The analysis service is configured to provide, as an example of impact information, superiority / inferiority information regarding the impact of each choice on winning the card game. The analysis service can be applied to appropriate situations including selection opportunities. For example, the analysis service is applied to selection opportunities included in the battle screen 50.

[0059] Specifically, a battle on the battle screen 50 is played by players repeatedly repeating turns including multiple phases, as shown in FIG. 4 . 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 areas 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] The superiority / inferiority information may be provided appropriately on the battle screen 50, for example, via an advice display field for displaying the superiority / inferiority information. The advice display field may be displayed appropriately, and may be displayed automatically without a request, or may be displayed in response to a predetermined request from the user (including selection of a specific play mode that includes the provision of superiority / inferiority information). The superiority / inferiority information may be appropriate information regarding the influence of each option, such as information indicating the degree of influence each option has on victory, such as A, B, C, etc. As an example, the superiority / inferiority information is configured as numerical information indicating the likelihood of progress being advantageous in the battle through numerical magnitude. The numerical information may be a numerical value indicating various advantages, for example, a numerical value indicating the likelihood of progress being advantageous (less likely to be disadvantaged) within a predetermined range of turns or choices to be analyzed. As an example, the numerical information is provided as a probability value indicating the possibility (probability) of ultimately winning the battle.

[0061] Furthermore, a card game may involve a series of selection opportunities where a continuous series of choices must be made. In the case of such a series of selection opportunities, the analysis service is configured to treat a combination of a series of choices in the series of selection opportunities as one selection set and provide influence information, i.e., probability values ​​(inferiority information), for each selection set. For example, in the case where a series of selection opportunities are provided where choices derived from the first selection in the first selection opportunity are made in order, the analysis service is provided to analyze the next choice that occurs by making the first selection in the first selection opportunity.

[0062] Specifically, for example, the first or second Main Phase (hereinafter, when there is no distinction between them, they may simply be referred to as the Main Phase) includes a selection opportunity for selecting the card object CO to be used and the placement destination (card area CP) of that card object CO. Furthermore, when a card object capable of activating an effect is selected, a selection may be required for whether or not to activate the effect and the destination of the effect. In other words, in order to activate the effect (or ability) of a single card object CO (to use it so that the progress associated with that effect is granted), a series of selections (multiple selections) may be required as a set (continuously). For this reason, the Main Phase includes a series of selection opportunities (multiple selection opportunities) for carrying them out. Other phases, such as the Battle Phase, may also include a series of selection opportunities in which a similar series of selections should be made.

[0063] Figure 5 is an explanatory diagram illustrating an example of a series of selection opportunities that may be included in the main phase. The example in Figure 5 shows a series of selections (flow) when using the effect of a predetermined magic card CO2 to summon one Extra Monster card CO1 from an Extra Deck card CO7 to the card area CP in the field area 54. As shown in Figure 5, the summoning in this case requires a series of selections from the first selection opportunity to the fifth selection opportunity.

[0064] First, in the first selection opportunity, any selection is permitted from the hand CO5, the card object to be used, or predetermined progress such as phase transition or turn end. The selection of the card object to be used is performed within an area that includes the available card object CO, such as the Graveyard area 58 or the excluded area EA. In the example of Figure 5, the activation of the effect of Magic Card CO2 (hereinafter sometimes referred to as "Fusion" Magic Card CO2), which has the effect of enabling the summoning of Extra Monster Card CO1, is selected in the first selection opportunity.

[0065] The "Fusion" Magic Card CO2 has the effect of summoning an Extra Monster Card CO1 to be summoned from an Extra Deck Card CO7 to the card area CP in the field area 54. This summoning is performed in exchange for sending a predetermined card object CO (material) from the card area CP to the Graveyard area 58. The predetermined card object CO is set in advance according to the Extra Monster Card CO1 to be summoned. Therefore, following the first selection opportunity (selection of the activated card), a second selection opportunity is given to select the Extra Monster Card CO1 to be summoned by the activated effect.

[0066] In the example of Figure 5, in the second selection opportunity (selection of summoned cards), an extra monster card CO1 is selected as the summon target, using two monster cards CO1 (often specific monster cards CO1) as materials. In this case, a third selection opportunity (first material selection) and a fourth selection opportunity (second material selection) for selecting the two materials (monster cards CO1) are provided in order, derived from the selection in the second selection opportunity. In the third and fourth selection opportunities, the two monster cards CO1 (first and second materials) that will be used as materials are selected, respectively. The range in which the two monster cards CO1 can be selected (which may vary depending on the contents of the magic card CO2, etc.) may be set appropriately, and, for example, is set to the card object CO in the card area CP and the hand CO5. In the example of Figure 5, a normal monster card CO1 is selected as material in both the third and fourth selection opportunities.

[0067] Following the selection of materials, a fifth selection opportunity is provided to select the card storage CP where the Extra Monster Card CO1 to be summoned should be placed (summoned). After the placement destination is selected in the fifth selection opportunity, the Extra Monster Card CO1 to be summoned, selected in the second selection opportunity, is summoned (placed) to the card storage CP selected in the fifth selection opportunity. In other words, the effect (progress) of summoning the Extra Monster Card CO1 actually occurs depending on the selection results (series of choices) in the series of selection opportunities from the first selection opportunity to the fifth selection opportunity.

[0068] Meanwhile, with the summoning of the Extra Monster Card CO1, the two Normal Monster Cards CO1 (first and second materials) selected at the third and fourth selection opportunities are sent to the Graveyard 58. As an example, various phases, such as the Main Phase, include a series of selection opportunities like this, depending on the effects of the card objects CO used. For such a series of selection opportunities, the analysis service provides impact information on a selection set basis. In other words, for a series of selection opportunities, rather than analyzing the impact of each choice for each selection opportunity, the service analyzes the impact for each selection set, and provides a probability value for each selection set as the analysis result.

[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 selection set output by the artificial intelligence model (analysis model unit 23) of the analysis service will be described. Fig. 6 is an explanatory diagram for illustrating a list of options (actions) that a player can take in a certain situation on the 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 are classified into multiple layers of option groups. Then, a probability value for each option is output for the option group in the deepest layer in each tree.

[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 include four: use an "A monster card," use a "B magic card," "phase transition," and "end turn." 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). For this reason, there are two options for each of the two card placement areas, CP1 and CP2: "Summon" and "Set." As a result, there are four options for using "A Monster Cards," corresponding to the two card placement areas CP x two placement methods.

[0072] On the other hand, for example, in the field area 54 in the example of Figure 5, if the back row 54B is divided into the 6th card place CP to the 10th card place CP from the left end to the right, and three magic cards CO2 etc. are placed in the 8th card place CP to the 10th card place CP, the card place CP where the "B Magic Card" (Magic Card CO2) can be placed is two: the 6th card place CP and the 7th card place CP. Therefore, these two card place CPs correspond to the options corresponding to the placement destination of the "B Magic Card".

[0073] The "B Magic Card" can be configured as any Magic Card CO2. The example in Figure 6 shows the case where the "B Magic Card" is the "Fusion" Magic Card CO2 from the example in Figure 5. The placement methods for the "B Magic Card" in the card storage area CP include two placement methods: "Activate" (the option to activate the effect) and "Set" (the option to withhold the activation of the effect). Therefore, these two placement methods exist as options. Consequently, there are also four options for using the "B Magic Card," corresponding to the two card storage areas CP and the two placement methods. These four options, plus the four options corresponding to the "A Monster Card," total eight options form the second-tier options group.

[0074] 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."

[0075] Similarly, when the effect of a "B Magic Card" is activated, options are derived for each placement location. Therefore, two options corresponding to each of the two card placement locations are generated from the second-tier options group for "B Magic Cards." These two options are then added to the four options derived from the second-tier options group for "A Monster Cards," resulting in a total of six options forming the third-tier options group. Note that for the Magic Card CO2, various other options may also exist depending on the situation. For example, if the destination of the effect can be specified, further options for selecting that destination may be generated. Although not shown in the example of Figure 6, the same applies to other card objects CO, such as the Trap Card CO3.

[0076] Furthermore, the third layer of options leads to the fourth layer of options, the fourth layer of options leads to the fifth layer of options, the fifth layer of options leads to the sixth layer of options, and the sixth layer of options leads to the seventh layer of options. When the "Fusion" effect in the example of Figure 5 is activated, a series of choices is required, including the selection of the Extra Monster Card CO1 to be summoned, the selection of materials, and the selection of a placement location. The candidates from this series of choices form the fourth through seventh layers. For example, if the Extra Deck Card CO7 contains two types of Extra Monster Card CO1, Extra Monster Card A CO1 and Extra Monster Card B CO1, these are candidates for summoning and function as options for the summon target. Therefore, four options corresponding to two card placement CPs x two candidates form the fourth layer of options.

[0077] If two specific Monster Cards CO1 are required to summon Extra Monster Card A CO1, when Extra Monster Card A CO1 is selected as the summoning target, the first material (first material) forms the 5th tier selection group. Similarly, if one specific Monster Card CO1 is required to summon Extra Monster Card B CO1, the first material (first material) forms the 5th tier selection group. As a result, the 5th tier selection group is formed by the four options that arise from each of the four options in the 4th tier selection group (two card storage CPs x two first materials corresponding to each of the two summoning candidates).

[0078] Furthermore, if the A Extra Monster Card CO1 is selected as the summon target, the player is required to select a second material (second material). Therefore, the selection of the second material is derived from the selection of the first material (fifth layer selection group). On the other hand, if the B Extra Monster Card CO1 is selected as the summon target, once the selection of the first material is complete, the player is required to select a card storage CP from which the summon target should be summoned. If the B Extra Monster Card CO1 can be placed in both the front row 54A and the common area 52 in the field area 54, then the first card storage CP, the second card storage CP, and the two card storage CPs in the common area 52 are candidates for placement. In other words, there are four options for the placement of the B Extra Monster Card CO1 (some are omitted in the example of Figure 6). Therefore, the six-layer options are made up of five options (one option when A Extra Monster Card CO1 is selected (second material) plus four options for the placement of B Extra Monster Card CO1) x two card locations (sixth card location CP and seventh card location CP), for a total of ten options.

[0079] If the A Extra Monster Card CO1 can be placed in both the front row 54A and the common area 52 in the field area 54, there are similar placement options as for the B Extra Monster Card CO1. In other words, there are four options for the placement of the A Extra Monster Card CO1. Therefore, these four options are derived from the selection of the second material (sixth layer options group). Therefore, four options x two card areas (sixth card area CP and seventh card area CP), a total of eight options, form the seventh layer options group.

[0080] 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 deepest layer options. Therefore, each option in these option groups is input into the artificial intelligence model, 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), part of the sixth layer options (when B Extra Monster Card CO1 is the summon target), and the seventh layer options correspond to the deepest layer options. Therefore, each option in these option groups is input into the artificial intelligence model, and a probability value is output for each option.

[0081] Each option in the first to seventh layer option groups is presented and selected in the first to seventh selection opportunities, respectively. In the example of FIG. 6, each option forming each selection set is shown expanded for each option, but each tree occurring from the second layer option group onward corresponds to a series of selections. For example, when an A monster card is used, the first to third selection opportunities for selecting the third option group (some of which are the second option group) from the first layer option group correspond to a series of selection opportunities. Similarly, when a B magic card is used, the first to seventh selection opportunities for selecting the seventh option group (some of which are the sixth option group) from the first total option group correspond to a series of selection opportunities. In either case, probability values ​​are calculated only for each option in the deepest layer option group. In other words, a series of selections in a series of selection opportunities is treated as one set (selection set), and a probability value for the final selection result of each selection set is output. In this example, the series of selection combinations when an A monster card is used and the series of selection combinations when a B magic card is used function as multiple selection sets in the present invention.

[0082] However, the example in Figure 6 shows a case where the summoner does not have the first material required to summon B Extra Monster Card CO1 (for ease of explanation, the example in Figure 6 also displays options when the first material is not available, but options may be omitted for impossible choices, such as when the first material is not available). Therefore, a zero is output as the probability value for all of the sixth-layer options derived from the fifth-layer options for B Extra Monster Card CO1. On the other hand, because the summoner has both the first and second materials required to summon A Extra Monster Card CO1, probability values ​​such as "8.0," "15.0," or "14.0" are output for the seventh-layer options (options derived from the sixth-layer options for A Extra Monster Card CO1).

[0083] 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 options group in the deepest layer. For this reason, the two options "phase transition" and "turn end" are input into the AI ​​model, and a probability value is output for each option.

[0084] The probability values ​​output by the analysis service may be used as appropriate, and may be provided to users as appropriate, for example, as information to assist beginners in playing. As an example, the probability values ​​output by the analysis service are used to determine the choices to be made by a computer acting as the user's opponent in AI battle mode. That is, in AI battle mode, the opponent (computer) determines the card object CO to be used based on the probability values ​​output by the analysis service and actually executes the determined choices. Furthermore, when a series of choices is requested in the analysis service, the probability values ​​are calculated for each combination of the series of choices (selection set). Therefore, in AI battle mode, a series of choices to be made by the opponent is determined for each selection set, and the determined series of choices is executed.

[0085] For example, in the example of FIG. 6, the "Second Card Placement (Common Area)" in the seventh layer of options has the highest probability value. Therefore, in the situation of the example of FIG. 6, the selection set to be executed is determined to be the one to be executed, which involves placing the B Magic Card CO2 in the sixth card placement CP so that its effect is activated, and summoning the A Extra Monster Card CO1 to the second card placement CP in the common area in exchange for the first and second materials, and this series of selections is executed. In this example, the selection set leading up to the "Second Card Placement (Common Area)" in the seventh layer of options functions as the selection set to be executed in the present invention. Furthermore, the series of selections corresponding to this selection set (B Magic Card, sixth card placement, effect activation, A Extra Monster Card CO1, first material, second material, and the second card placement CP in the common area) functions as the series of selections to be executed in the present invention. Furthermore, the first selection opportunity to execute the first selection in each selection set (first layer of options), or each selection opportunity up to the final selection, functions as a selection opportunity in the present invention. Similarly, each subsequent choice opportunity following the first in each choice set serves as a subsequent choice opportunity of the present invention.

[0086] Note that the example in FIG. 6 shows an example of a group of options in the main phase. Therefore, in addition to a phase end instruction, 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, are shown. However, options according 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, the end of the turn, etc. may be the same) are different. In other words, the options available in each phase are different. Then, information on the probability value of each option available for each phase is output.

[0087] 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.

[0088] The AI ​​model for the analytical service (analysis program AP) may be generated appropriately using various learning methods, such as the TD method, which is a type of reinforcement learning. The TD method may also include techniques such as SARSA. For example, the Q-learning method is used to generate the AI ​​model for the analytical service.

[0089] 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.

[0090] Furthermore, the reward (a predetermined result obtained by an action, which may be included in the algorithm of the learning program) that is the target of the Q value may be set appropriately, and for example, 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 also have different ways of thinking depending on the reward (predetermined result) in Q-learning. As an example, the AI ​​model for the analysis service is configured to calculate the probability value (Q value) for winning a match using the DQN method. In other words, in the AI ​​model for the analysis service, winning a match is set as an example of a reward.

[0091] 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. A system often includes multiple hidden 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 a layer that outputs the results of calculations performed in the hidden layers. An 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 "◯".

[0092] 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 placer CP to the tenth card placer CP, the situation log SL may also contain 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).

[0093] 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. An appropriate number of output values ​​can be calculated in the first intermediate layer. In the example of Figure 7, four output values ​​are calculated. Furthermore, a predetermined function formula using appropriate weighting is applied to the four output values, and the output values ​​of the intermediate layer (second layer) are calculated using this function formula. An appropriate number of output values ​​can also be calculated in the second intermediate layer. In the example of Figure 7, four output values ​​are also calculated. Note that the weighting (weight value w) corresponding to each connection line is calculated as a learning result and is managed, for example, in the learned data for analysis AD. Note that the connection lines (synapses) connecting the input layer and intermediate layer are omitted as appropriate in Figure 7.

[0094] The output value of the final intermediate layer (second layer) is calculated as a Q value using a predetermined function formula that uses 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 in the example of FIG. 7. If an option requires a series of selections, the Q value is calculated for each series of selections (selection set). The calculated Q value may be 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%.

[0095] (Special rules) Next, with reference to FIG. 8, an example of a special rule provided for a card game will be described. A card game includes various special rules in addition to basic rules (for example, the example of FIG. 4). A card game includes a chain rule as an example of a special rule. The chain rule is a rule for smoothly resolving the exchange of various card objects CO, such as magic cards CO2 and trap cards CO3. Specifically, the chain rule is a rule that makes it possible to activate one card object CO in response to the activation of another card object CO. When the effect of a card object CO is activated, the chain rule always provides the opposing player with a choice opportunity (hereinafter sometimes referred to as a counter-choice opportunity) to make a choice to counter the activation of the effect. During the counter-choice opportunity, the player is naturally allowed to choose to do nothing (not to perform a counter-action).

[0096] If the opposing player makes a counter-choice choice (hereinafter sometimes simply referred to as a chain) during the counter-choice opportunity, then it becomes possible for the player to make a chain. If the opposing player does not make a chain, it is also possible for the player to make a chain themselves (select a card object CO to activate an effect on their own card object CO that has activated its effect). Chains can be stacked as much as possible. Then, when neither player is making a chain, the process of activating effects is executed in order, starting with the card object CO that last activated the chain. In other words, the activation of the effects of each card object CO used as an activation target in a chain is suspended (stacked) until the final chain is executed. Then, with the final chain, each of the suspended effects is activated in order, starting with the card object CO in that final chain, in reverse chronological order of use.

[0097] The analysis service calculates probability values ​​for each selection set, and these probability values ​​are used to determine a series of selections that an opponent should make in a series of selection opportunities in the AI ​​battle mode. However, when a chain is executed according to the chain rule, there may be cases where a series of selections determined based on the probability values ​​becomes impossible to execute. In this case, an alternative selection to the impossible selection is determined.

[0098] FIG. 8 is an explanatory diagram illustrating an example of a chain that can occur after the summoning of the Extra Monster Card CO1. For example, if both the first and second materials in FIG. 5 have effects that can be activated by moving them to the Graveyard 58 upon summoning, a chain can occur after the summoning in the example in FIG. 5. Specifically, in the example in FIG. 5, the Extra Monster Card CO1 is summoned by the effect of the "Fusion" Spell Card CO2, and the first and second materials are sent to the Graveyard 58 for this summoning. Therefore, if the first and second materials have effects that require them to be moved to the Graveyard 58, the effects of the first and second materials can be activated as their own chain in conjunction with the summoning effect. The example in FIG. 8 illustrates this case as an example. The example in FIG. 8 also illustrates a case in which one of the series of choices determined based on the analysis service becomes impossible to execute due to the user's chain.

[0099] As shown in Figure 8, if the above-mentioned effects are set for both the first and second materials in Figure 5, after the summoning of the Extra Monster Card CO1, the summoner (for example, the computer in AI battle mode. Hereinafter, the role of the computer in AI battle mode may be referred to as COM) will have two chain opportunities. The two chain opportunities are opportunities to make choices regarding the effects of the two materials, respectively. Hereinafter, they may be referred to as the first chain and the second chain in chronological order.

[0100] The first chain includes three options: activate the effect of the first material, activate the effect of the second material, or do nothing (activate neither effect). In the example of Figure 8, the first material has the effect "●●" set on the condition that it is moved to the Graveyard 58 ("Graveyard"). Therefore, when the first material's effect is selected, the "●● effect" is activated. Meanwhile, the second material has the effect set on the condition that it is moved to the Graveyard 58, adding either a face-up D Monster card CO1 ("D Monster") from the Extra Deck Card CO7 or a C Monster card CO1 ("C Monster") from the Graveyard 58 to the hand CO5. Therefore, when the first material's effect is selected, two options corresponding to the addition target (D Monster card CO1 and C Monster card CO1) are derived from that selection. Various further options can arise from the two addition target options, but details are omitted in the example of Figure 8.

[0101] The unit of the selection set analyzed by the analysis service may be set as appropriate. For example, the selection of the "Fusion" Spell Card CO2 in the example of FIG. 5 to the selection of the second chain (including the selection of additional targets within each chain) in the example of FIG. 8 may be set as one selection set. Alternatively, the selections up to the end of the summoning in the example of FIG. 5 may be set as one selection set (series of selections), and the series of selections from the first chain to the second chain may be set as a separate selection set. In this case, the series of selections may be further separated into chains, and the series of selections within each chain, such as the selection of additional targets, may be set as one selection set. In the example of FIG. 8, the series of selections from the first chain to the second chain are separated from the selection set up to the end of the summoning, but are not separated by chain, and the series of selections from the first chain to the second chain are set as one selection set. Therefore, the series of selections from the first chain to the second chain are determined after the summoning via the analysis service.

[0102] In the first chain, an appropriate selection may be determined based on the analysis results of the analysis service. In the example of FIG. 8, of the three options, activating the effect of the first material is determined as the selection to be executed. In this case, the second chain includes two options: activating the effect of the second material and doing nothing (not activating the effect of the second material). In the second chain, the game information used in the first chain in the analysis by the analysis service may be used as is. In this case, it is assumed that the probability values ​​for the series of selections for the second material calculated in the first chain will be similar to the analysis results in the first chain. Therefore, the analysis by the analysis service may be omitted in the second chain. In the example of FIG. 8, as an example, the analysis service in the second chain is executed with the game information used in the first chain reflecting the selection results of the first chain. In other words, in the second chain, probability values ​​for the remaining two options, activating the effect of the second material and doing nothing, are calculated assuming that the selections in the first chain will be executed (since the probability values ​​are calculated for the selections up to the second chain as one selection set, the selections in the first chain have not yet been executed). In the example of FIG. 8, the selection to be executed in the second chain is to activate the effect of the second material, and the C monster card CO1 in the graveyard area 58 is determined as an additional target in association with the activation.

[0103] Under the chain rule, an opposing player (e.g., a user) is given a chance to make a chain against the COM for each chain, where they can make a choice that counters the COM's choice. However, a series of choices in the first and second chains are treated as a single selection set, and a probability value is calculated, and each choice is determined. Therefore, the COM does not take the opposing player's chain into consideration when determining the choices to be made in the first and second chains. However, after each choice is actually made, there is a possibility that the opposing player's chain will be made. In the example of Figure 8, the opposing player's chain is not made after the choice to be made in the first chain is determined, but after the selection of the second chain, the opposing player (user) makes a choice (third chain) that counters the second chain.

[0104] Specifically, the user uses a magic card CO2 as the third chain after the second chain. This magic card CO2 has an effect that, if this magic card CO2 is in the hand CO5, it can be removed from the graveyard area 58 of the COM (the user's opponent) on the condition that it is sent (discarded) to the graveyard area 58. A C monster card CO1 in the graveyard area 58 of the COM is selected as the target for activating the effect of this magic card CO2.

[0105] According to the chain rule, the effects of the choices made in each chain are suspended (stacked) during the chain and are executed in reverse order after the final chain. If the third chain is the final chain, the effects of the third chain, second chain, and first chain are executed in this order after the third chain. Therefore, the effect of the Magic Card CO2 in the third chain is activated first, and the C Monster Card CO1 in the COM's Graveyard 58 is banished from the Graveyard 58. In the next second chain, a choice to add the C Monster Card CO1 from the Graveyard 58 to the hand CO5 (activation of the effect of the second material) was planned. However, due to the effect of the third chain, the C Monster Card CO1 that was scheduled to be added is banished from the Graveyard 58 and cannot be added. In other words, the addition of the C Monster Card CO1, one of a series of choices determined based on the analysis service, is made impossible by the user's chain. In this case, the COM determines the choices after the impossible choice according to predetermined rules.

[0106] The predetermined rule may be any appropriate rule for determining another selection. For example, the predetermined rule may be configured to determine a selection with a probability value subsequent to the initial selection based on the probability values ​​of other selections calculated to determine the initial selection. Alternatively, a lottery result may be used as the predetermined rule. If the initially determined selection is impossible, a lottery is held, and another selection may be randomly determined based on the lottery result. In the example of FIG. 8, if the C Monster Card CO1 is impossible to select, the D Monster Card CO1 is the only option left. Therefore, the D Monster Card CO1 is the addition target in the second chain anyway, and the lottery result is used as an example of a predetermined rule. Then, upon activation of the effect of the second chain, the D Monster Card CO1 is added to the hand CO5. Furthermore, after activation of the effect of the second chain, the "●● effect" of the first chain is activated. The series of selections when a chain is applied is realized, for example, by the procedure described above. In this example, the COM and the user function as multiple players of the present invention, and also function as one player and another player, respectively. Also, the selection of C monsters in the second chain functions as one selection of the present invention.

[0107] (Network system processing) 9 and 10, a selection execution process and a selection redetermination process will be described as examples of the processes of the network system 1. As an example, both the selection execution process and the selection redetermination process are realized by the COM battle realization unit 25 of the game server 2 and the progress control unit 33 of the user device 3 in cooperation with each other. For this reason, in FIGS. 9 and 10, the process mainly executed by the COM battle realization unit 25 is shown as the game server 2, and the process mainly executed by the progress control unit 33 is shown as the user device 3.

[0108] The selection execution process is a process for executing various selections in the AI ​​battle mode. The selection execution process is configured to execute various selections using an analysis service. When a selection opportunity arrives for the COM in a battle in the AI ​​battle mode, the progress control unit 33 starts the selection execution process of FIG. 9 and first acquires game information related to the current game situation required for calculating the probability value (S101). As an example, the game information is managed by a situation log SL. Therefore, the progress control unit 33 updates (including generates) the situation log SL in step S101 so as to record the acquired game information.

[0109] Next, the progress control unit 33 requests the game server 2 to decide on the selection in the target selection opportunity (step S102). This request includes the latest situation log SL. In other words, this request includes the game information acquired in step S101.

[0110] When a request is sent from the progress control unit 33, the COM match realization unit 25 starts the selection execution process of FIG. 9 and first acquires the request (including game information) (step S201). Subsequently, the COM match realization unit 25 provides the game information acquired in step S201 to the artificial intelligence model (analysis model unit 23) so that the analysis service is executed (probability values ​​are calculated) (step S202). Next, the COM match realization unit 25 acquires the analysis results (probability values) from the artificial intelligence model (analysis model unit 23) (step S203). That is, it acquires probability value information for each option (or for each selection set in the case of a series of selection opportunities) using the artificial intelligence model. Furthermore, the COM match realization unit 25 determines the selection to be executed in the target selection opportunity based on the probability value information acquired in step S203 (step S204). The COM match realization unit 25 may appropriately determine the selection based on the probability value. For example, it determines the option with the highest probability value as the execution target. A similar determination is made in the case of a series of selection opportunities. That is, the selection set with the highest probability value among the selection sets is determined as the series of selections to be executed in the series of selection opportunities. Then, the COM battle realization unit 25 transmits the determination result determined in step S204 to the progress control unit 33 (step S205), and the current selection execution process is terminated.

[0111] On the other hand, when the determination result is transmitted from the COM battle realization unit 25, the progress control unit 33 acquires the determination result (step S103). Next, the progress control unit 33 executes a selection in the target selection opportunity in accordance with the determination result acquired in step S103 (step S104). If the target selection opportunity is a series of selection opportunities, the progress control unit 33 executes a series of selections corresponding to the selection set as the determination result in the series of selection opportunities in order. Then, after this selection, the progress control unit 33 ends the current selection execution process.

[0112] The procedure in FIG. 9 realizes selection (battle) using an artificial intelligence model (analysis service) in the AI ​​battle mode. Furthermore, analysis by the artificial intelligence model is performed on a selection set basis during a series of selection opportunities, and a series of selections of execution targets are determined for each selection set based on the analysis results. In the example of FIG. 9, information on the selection sets of execution targets during a series of selection opportunities is collectively transmitted to the user device 3 as determination results in step S104, but this is merely an example. For example, the selection execution process may be configured such that an inquiry about the selection of an execution target is made from the user device 3 to the game server 2 for each selection opportunity during a series of selection opportunities, and a selection corresponding to the selection opportunity in the selection set of execution targets (determination results) is transmitted as the execution target each time.

[0113] The selection redetermination process is a process for redeterminating an unexecutable selection when a chain is executed during a series of selections (a selection set to be executed) and the series of selections determined in the selection execution process (or, in some cases, the selection determined in the reselection determination process) cannot be executed. Each time a selection in the series of selections (including a selection determined in the reselection determination process) is executed, the progress control unit 33 starts the selection redetermination process of FIG. 10 and determines whether an unexecutable selection has occurred in the determined series of selections (step S301). If an unexecutable selection has not occurred (S301: No), the progress control unit 33 skips the subsequent processes and terminates the current reselection determination process.

[0114] On the other hand, if an unexecutable selection has occurred (S301: Yes), the progress control unit 33 acquires current game information (step S302). This process is executed in the same manner as step S101 in the example of FIG. 9. Next, the progress control unit 33 requests the game server 2 to redetermine the remaining unexecutable selections (selections after the unexecutable selection) (step S303). This request includes the latest situation log SL (game information acquired in step S302).

[0115] When a request is sent from the progress control unit 33, the COM match realization unit 25 starts the selection redetermination process of FIG. 10 and first receives the request (step S401). Next, the COM match realization unit 25 redeterminates the remaining selections (step S402). The COM match realization unit 25 may execute the redetermination using an analysis service, but as an example, executes the redetermination according to a predetermined rule. The predetermined rule may be any appropriate rule. For example, it may be a rule that determines the remaining selections so as to transition to the selection with the second highest probability value based on the probability value output when the selection set is determined in the selection execution process. As an example, the predetermined rule is set to a rule that determines the remaining selections randomly (by lottery) from the currently selectable options. Therefore, the COM match realization unit 25, while referencing the game information acquired in step S401, draws one of the currently selectable options by lottery and determines the remaining selection to be the drawn option. If there are multiple remaining options, each of them is determined by a similar lottery for each selection. Then, the COM match realization unit 25 transmits the determination result determined in step S403 to the progress control unit 33, and the current selection redetermination process ends.

[0116] On the other hand, when the determination result is transmitted from the COM battle realization unit 25, the progress control unit 33 acquires the determination result (step S304). Next, the progress control unit 33 executes the remaining selections in the target selection opportunity in accordance with the determination result acquired in step S304 (step S305). Then, after this selection, the progress control unit 33 ends the current selection redetermining process. As a result, if a series of determined selections becomes impossible to execute due to a chain, the remaining selections that became impossible to execute are redetermined and executed. In other words, a series of selections that utilizes an artificial intelligence model (analysis service) is permitted even under the chain rule.

[0117] As described above, according to this embodiment, information on the probability value of a selection in each selection opportunity is obtained using an artificial intelligence model (AI). In the case of a series of selection opportunities, a series of combinations of selections in the series of selection opportunities is treated as one selection set, and information on the probability value regarding the winning rate that each selection set gives to the game is obtained. In other words, for the selections in the series of selection opportunities, probability value information is obtained for each combination of selections. Therefore, for a series of selections in a series of selection opportunities, more appropriate probability value information regarding winning can be obtained compared to when a probability value is calculated for each selection opportunity and each option for each selection. In particular, in a series of selection opportunities, selections of the type in which options are derived in order from the selection in the first selection opportunity are made. This type of selection often tends to be complex. As a result, more appropriate probability value information can be obtained for selections that tend to be complex.

[0118] In addition, in the AI ​​battle mode, a selection set to be executed in a series of selection opportunities is determined based on a probability value. Then, a series of selections corresponding to the selection set to be executed are sequentially executed as selections by the COM (the player acting as the user's opponent). In other words, a series of selections corresponding to the selection set to be executed determined based on the probability value are actually realized. This allows the battle (game) to proceed based on the probability value calculated by the artificial intelligence model. In other words, a so-called computer (AI) battle can be realized, utilizing the influence of each selection set on victory.

[0119] Furthermore, if a series of choices determined based on probability values ​​becomes unfeasible due to the chain rule, a feasible choice is determined in place of the unfeasible choice according to a predetermined rule. In other words, since the series of choices is complemented by the determination of alternative choices, the chain rule does not prevent the acquisition of probability values ​​on a choice set basis. Therefore, even in a match (game) that includes the chain rule (opponent choice opportunity), it is possible to acquire probability values ​​on a choice set basis.

[0120] In the above embodiment, the analytical model unit 23 of the game server 2 functions as a predetermined process of the present invention. Furthermore, the COM match realization unit 25 of the game server 2 functions as an information acquisition means and an influence acquisition means of the present invention by executing the procedure of FIG. 9. Specifically, the COM match realization unit 25 functions as an information acquisition means by executing step S201 of FIG. 9, and as an influence acquisition means by executing step S203. Furthermore, the COM match realization unit 25 of the game server 2 functions as a selection determination means of the present invention by executing the procedures of FIG. 9 and FIG. 10. Specifically, the COM match realization unit 25 functions as a selection determination means by executing step S204 of FIG. 9 and step S402 of FIG. 10.

[0121] 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, a series of selection opportunities is provided so that options derived in order from a selection in a first selection opportunity are selected. However, the present invention is not limited to such embodiments. For example, a series of selection opportunities may be provided so that a selection in any of the selection opportunities is derived in a later selection opportunity. Alternatively, a series of selection opportunities may be provided so that options in later selection opportunities change (including both changes in number and content) depending on the selection result in an earlier selection opportunity. In other words, each selection opportunity in a series of selection opportunities is not limited to being derived, and may have an appropriate relationship. Furthermore, a series of selection opportunities may not have a relationship with each other. In other words, a series of selection opportunities may be an appropriate concept applied to multiple selection opportunities that should be treated as a set.

[0122] In the above embodiment, a battle between a user and a computer in the AI ​​battle mode is described. However, the present invention is not limited to this embodiment. In the AI ​​battle mode, an appropriate battle between multiple players may be executed. For example, the multiple players may be different artificial intelligence models with different ways of thinking. In other words, the AI ​​battle mode may be applied to a battle between multiple computer.

[0123] In the above-described embodiment, the network system 1 is configured such that the game server 2 (including a case where the network system 1 is configured with multiple server devices) functions alone as the game system of the present invention. However, the present invention is not limited to this embodiment. For example, the user device 3 may perform all or part of the role (various processes) of the game server 2. When the user device 3 performs all of the role of the game server 2 (for example, the processes of FIGS. 9 and 10), the user device 3 may function alone as the game system of the present invention. In this case, the user device 3 may be configured as an offline game device that is played without being connected to the network NT. The game server 2 may be omitted. Alternatively, when part of the role of the game server 2 is performed by the user device 3, the combination of the user device 3 and the game server 2 (including the network system 1) may function as the game system of the present invention. A program and a control method implemented in a device 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.

[0124] FIG. 11 is a flowchart showing an example of the procedure for the AI ​​battle processing. The AI ​​battle processing is a process for executing a COM selection in the AI ​​battle mode in place of the selection execution process of FIG. 9 and the selection redetermination process of FIG. 10 when an artificial intelligence model (analysis model unit 23) is provided in the user device 3 (hereinafter, the analysis model unit of the user device 3 may be referred to as a terminal analysis model unit to distinguish it from the analysis model unit 23). Furthermore, each selection set may be set as a series of selections in an appropriate unit. For example, as described above, a series of selections from the first chain to the second chain may be set as one selection set. The example of FIG. 11 shows a case where a series of selections in each chain is set as one selection set, i.e., a case where a probability value for a series of selections is calculated for each chain.

[0125] When a selection opportunity arrives for the COM in a battle in the AI ​​battle mode, the progress control unit 33 acquires game information about the current game situation and requests the terminal analysis model unit to execute a selection along with the game information. When this request occurs, the terminal analysis model unit acquires the request (including the game information) and starts the battle AI processing of Figure 11, first determining whether there is a determined selection to be executed at the target selection opportunity (step S501).

[0126] If a determined selection exists (step S501: Yes), the device analytical model unit determines whether the determined selection is selectable based on the game information acquired in step S501 (step S502). For example, since a series of selections have already been determined in a series of selection opportunities, it is determined in step S501 that a determined selection exists. Then, it is determined in step S502 whether that selection is selectable. For example, as in the example of FIG. 8, if the determined selection in Chain 2 becomes unexecutable due to the activation of the effect of Chain 3, it is determined that the selection is unexecutable. On the other hand, for example, since the activation of the effect of Chain 1 makes the selection possible even after the activation of the effect of Chain 3, it is determined that the determined selection is selectable. If the determined selection is selectable (step S502: Yes), the device analytical model unit executes the determined selection in the target selection opportunity (step S503). After this execution, the device analytical model unit terminates the current battle AI processing.

[0127] On the other hand, if the determined selection is not selectable (step S502: No), the device analytical model unit, while referring to the game information, randomly (by lottery) determines the selection to be executed from the options selectable in the current situation (step S504), and executes the determined selection at the target selection opportunity (step S505). After this execution, the device analytical model unit terminates the current battle AI processing. Note that all or part of the probability value (the determination result of step S506) used when determining the determined selection may be saved, and in that case, the processing of step S504 may be configured to select the option with the next highest probability value from the selectable options based on the saved probability value.

[0128] On the other hand, if there is no determined selection in the target selection opportunity (step S501: No), the terminal analysis model unit determines the options that can be taken in the current selection opportunity (step S506). More specifically, the terminal analysis model unit determines the options that can be taken in the current selection opportunity, for example, each option corresponding to the first layer option group in the example of Figure 6.

[0129] Next, the terminal analysis model unit determines options derived from the possible options (first layer option group) determined in step S506 (step S507). More specifically, the terminal analysis model unit determines options from the second layer option group onwards that arise from the first layer option group (options at the first selection opportunity) in the example of Fig. 6, for example, i.e., each option in a series of selection opportunities.

[0130] Next, the terminal analysis model unit performs an analysis of each option (step S508). More specifically, the terminal analysis model unit calculates a probability value for each option group in the deepest layer based on the determination results of steps S507 and S506.

[0131] Next, the device analysis model unit determines the choice to be made in the target selection opportunity based on the analysis result of step S508 (step S509). More specifically, the device analysis model unit determines the option with the highest probability value (highest probability of winning) as the choice to be made based on the probability values ​​calculated in step S508. If the option with the highest probability value is the last choice in a series of selection opportunities, the device analysis model unit determines the series of choices leading up to the last choice as the choice to be made, and determines the first choice in the series of choices as the choice to be made in the target selection opportunity.

[0132] Furthermore, the device analytical model unit determines whether a chain is possible after the selection in the target selection opportunity (step S510). If a chain is possible (step S510: Yes), the device analytical model unit updates the game information by assuming that the selection to be executed determined in step S509 has been executed (step S511), returns to step S506, and executes the subsequent processes again. For example, if the target selection opportunity is a selection opportunity where a selection corresponding to the first chain in the example of FIG. 8 should be executed, the device analytical model unit determines the selection (series of selections) to be executed in the first chain in steps S506 to S509, and then reflects the determination result in the game information in step S511, returns to step S506, and executes the subsequent processes again for the second chain. The same applies when chains subsequent to the second chain are executed.

[0133] On the other hand, if chaining is not possible (step S510: No), the device analysis model unit executes the selection determined in step S509 at the target selection opportunity (step S512). For example, if selections to be executed at multiple selection opportunities including a series of selections, such as the selections to be executed in the first and second chains in the example of FIG. 8, are determined in step S509, the first of those selections to be executed is executed at the target selection opportunity. After this execution, the device analysis model unit terminates the current battle AI processing. This realizes the selection of a COM in the AI ​​battle mode.

[0134] In the example of Fig. 11, the terminal analysis model unit executes only the processing of step S508, and other processing may be executed by the progress control unit 33 or the like. Also, for example, when the first chain to the second chain in the example of Fig. 8 are determined to be one option set, or when the chain rule is applied, the processing of step S510 in the example of Fig. 11 may be omitted as appropriate. Furthermore, the processing of Fig. 11 may be executed as the processing of the analysis model unit 23 in the example of Fig. 9. In that case, the processing of Fig. 10 may be omitted. [Example]

[0135] Examples related to the computer programs and the like according to the above-described embodiments will be described below, but the technical scope of the present invention is not limited to the following examples.

[0136] (UDI) When developing AI, an interface is required to access the game client (hereafter referred to as the client) from an external environment such as Python. For this reason, we developed a function called the "Universal Duel Interface" (hereafter referred to as UDI). Figure 12 shows an overview of the UDI system configuration. UDI is a function that exchanges duel (match) information and input currently required by the client between the client and AI. There are two types of clients: card games (game programs) and console simulators designed for high-speed processing. The client sends the following information to the AI ​​through UDI when input is required or when the duel has ended.

[0137] Current Duel Information: Information about the revealed state of the board, such as the hand, field, graveyard, and extra deck, as well as player information such as LP (Life Points). -Duel history: A duel log listing card movements during the duel Executable commands: Summon, activate, decide, etc.

[0138] The AI ​​receives information from the client and transmits the following information through UDI: - Response to input requested by the client A rating for each executable command (selection) or group of commands (selection set), along with text describing its content -Predicted face-down card and its probability value

[0139] The client progresses the duel based on the information sent by the AI. If the client is a card game (game program), it displays the evaluation value of the action and the predicted hidden card.

[0140] (Problem setting) The decks used in the match were one centered around the card "Black Magician" and the other centered around the card "Blue-Eyes White Dragon." Hereafter, we refer to these as the "DM Deck" and "BE Deck," respectively. The goal of this example was to train an AI without using the user's play log. Therefore, an agent to serve as an opponent or model for efficient learning was required. Therefore, in addition to the currently under development COM (a computer incorporating an AI currently under development; hereafter referred to as COM), we created a rule-based AI specifically for this deck. The rule-based AI thinks faster than COM, contributing to improved learning efficiency. Furthermore, because it is specialized for this deck and stronger than COM, it is an excellent model for modeling. Figure 13 shows the win rates for 2,000 matches against COM. Significant differences are marked with an asterisk. A Z-test was performed at a 5% significance level to determine whether the win rates were different from 50%, and significant differences were observed for all combinations. To reduce the impact of hand quality, each player's hand was fixed and the first and second players were swapped for each match.

[0141] (Model Architecture) A proven value-based method was used as the algorithm for reinforcement learning. A neural network was used as the function model that outputs the action value Q. In card games, the actions that can be selected at each time are variable. Therefore, a model was adopted that takes state features and action features as input and outputs a single Q value for that action option. When selecting an action, input features are concatenated in batch direction for the number of options, and the action with the largest output Q value is executed. The method for creating the feature vector will be described later, but the size of the input features is 5294, and one Q value is output through a five-layer fully connected network (with intermediate layer sizes of 2048, 768, 256, and 128).

[0142] (feature) Figure 14 is a table showing some of the input features. Regarding game information such as each other's LP, numerical information was normalized to between 0 and 1, and other information was converted to one-hot data before use. Regarding deck and board information, since the card pool is fixed in this case, one-hot vectors and bitmaps of card IDs were mainly used.

[0143] We used special ingenuity when it came to chains. Depending on the chain, a certain action can result in a transition to a different state, making it difficult to model within a typical reinforcement learning framework. We therefore addressed this by including chain information in the state features. For example, if no chain is occurring, the chain vector is filled with all zeros. On the other hand, when a chain is occurring, the one-hot vectors of the cards that activated it and the cards affected by its effect are added as chain vectors to the input features. This allows us to express the transition, "As a result of taking a certain action, we transitioned to a state where a chain is occurring." Furthermore, since the chains in this card pool do not pile up very deeply, we added information about the most recent five chains to the state features.

[0144] We also devised ways to improve the action characteristics. Card games have a variety of card effects, many of which can be considered effects that change the location of a card. For example, the effect of special summoning a monster from the graveyard can be considered an effect that changes the location of the monster from the graveyard to the monster zone (the card area CP in the front row 54A of the field area 54 in the example in Figure 3). Therefore, we added a vector indicating the card ID to be moved and the location from which it was moved to the action characteristics. In addition, there are effects that strengthen allies and negate opponents' effects, so vectors have been prepared for those as well. We also consider the normal summon, attack, display change of monsters, and the set of various cards to be effects, and have prepared vectors for them as well.

[0145] (Learning method) The learning algorithm used was a slightly modified Monte Carlo method. Rewards were given only at the end of a duel, with a -1 for a loss, a 0 for a draw, and a +1 for a win. The discount rate was set to 1.0 to achieve the goal of ultimately winning the duel.

[0146] The specific steps are shown below. (1) Create a list for saving data. (2) After a game is completed, the transitions (state features, selected action features, rewards) for each time point are obtained. The rewards are either -1, 0, or +1 at all times. (3) The obtained data is stored in a storage list. (4) Repeat (2)-(3) until the number of data items in the list exceeds 1024. (5) Randomly select 64 batches of data from the list and train the model. This process is repeated 8 times. Note that the data is not reused. (6) Repeat steps (2)-(5) more than 2000 times.

[0147] The loss function used was the sum of the squared errors between the model output and the reward. The optimizer used SGD, with L2 regularization. Hyperparameters such as the regularization coefficient and learning rate were changed empirically depending on the progress of learning.

[0148] The training data was generated by combining imitation learning and reinforcement learning. In imitation learning, transition data was generated by having the rule-based AI play matches. This allows the Q-value when acting according to the rule-based AI's policy to be learned. In reinforcement learning, matches were played according to the model's output to generate transition data. During this time, no exploration was performed, and the action that maximized the Q-value was continuously selected. In addition, as an intermediate method between these, a method was used that switches between action selection by the rule-based AI and action selection according to the model's output with a certain probability during the match. The learning process mainly proceeded in the order of imitation learning, intermediate method, and reinforcement learning.

[0149] Furthermore, the models learned using these methods were saved, and the model with the highest win rate against the computer or the rule-based AI was repeatedly trained. The opponents were mainly changed in the order of computer, rule-based AI, and previous opponent AI. The decks used were fixed for each model, and the opponent's decks were randomly swapped. Approximately 100,000 matches were used for learning.

[0150] (result) Figure 15 is a table showing the results of 4,000 matches against the COM at the end of learning. A Z-test was performed at a significance level of 5% to see if there was a difference in win rate from 50%, and if a significant difference was found, an asterisk (*) is added. The AI ​​for DM decks was able to win against the COM, but the AI ​​for BE decks remained at the same strength or lower than the COM.

[0151] Next, we observed actual play to analyze the qualitative characteristics of the strategies learned by the AI. Although we found some insufficient learning, such as not attacking in situations where attacking would have won, we were able to confirm cases where combos could be executed just like the rule-based AI. Additionally, the rule-based AI implemented theories such as "not setting Spell and Trap Cards before entering the Battle Phase," and we were able to observe that this behavior, while imperfect, had been learned to a certain extent.

[0152] (Consideration) Using imitation learning and reinforcement learning, we have achieved the learning of an AI that can play card games with a certain level of skill. The winning rate is lower than that of rule-based AI, and the AI ​​sometimes behaves in a strategically unsound manner, so it is thought that the learning is insufficient. However, since the AI ​​can hardly win against the computer when playing randomly, it can be said that the method used in this example has successfully learned to play the game.

[0153] Although the winning rate was lower, there were two advantages that rule-based AI could not have. First, by visualizing the Q value, it was possible to express something like the AI's state of mind. In this problem setting, the Q value reflected the winning rate. Therefore, the audience could interpret that if the Q value was high, the AI ​​was confident of winning, and if it was low, the AI ​​was not confident.

[0154] Next, I showed the possibility of measuring the differences in characteristics between decks. From my own experience playing, I found that a DM deck can win if it employs a consistent strategy regardless of the opponent, whereas a BE deck requires flexible and precise strategies depending on the opponent. Therefore, it can be inferred that a BE deck is more difficult to learn, which led to the difference in win rates against the control between the AI ​​for DM decks and the AI ​​for BE decks. It is interesting to note that this suggests that the difficulty of learning may vary depending on the deck.

[0155] Regarding insufficient learning, there is room for improvement, particularly in the learning algorithm. The reinforcement learning in this method does not involve exploration, and unlike typical reinforcement learning approaches, it is thought that the policy obtained through imitation learning is actually fine-tuned. This method was adopted to create an AI of a certain level of strength in a short period of time in problem settings with a large search space and sparse reward design, such as card games. Furthermore, since learning was primarily based on increasing the win rate against computer opponents, there is a possibility that the AI ​​will not perform well when the opponent changes. Therefore, it is felt that there is a need to further improve self-play by incorporating exploration that does not rely on rule-based AI and league matches, etc., in order to develop a more general-purpose AI.

[0156] 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.

[0157] The computer program (PG1) of the present invention is configured to cause a computer (21) incorporated in a game system (2) that provides a game that progresses according to the results of selections made in a series of selection opportunities to function as an information acquisition means (25) that acquires game information indicating the state of the game necessary to obtain influence information regarding the influence that each selection set has on the game, with a combination of a series of selections made in the series of selection opportunities being treated as one selection set, and an influence acquisition means (25) that acquires the influence information regarding each selection set by utilizing a predetermined process (23) that outputs the influence information based on the game information.

[0158] According to the present invention, a predetermined process is used to acquire influence information regarding the influence that each combination of selections in a series of selection opportunities has on the game, with each combination of selections being treated as a single selection set. In other words, influence information is acquired for each combination of selections in a series of selection opportunities. This makes it possible to acquire more appropriate influence information for a series of selections in a series of selection opportunities.

[0159] The influence information may be used as appropriate. For example, it may be provided to the user as auxiliary information for playing the game. Alternatively, when a game is played by a team including the user, the influence information may be used to determine a series of choices to be made by one of the team's teammates. Similarly, the influence information may be used to determine a series of choices to be made by the user's opponent in a competitive game. Specifically, for example, as one aspect of the computer program of the present invention, a computer program may be employed that causes the computer to further function as a selection determination means (25) that determines one of the multiple selection sets as a target selection set based on the influence information, so that a target series of selections corresponding to the target selection set among the multiple selection sets is performed in the series of selection opportunities. In this case, a series of selections corresponding to the target selection set determined based on the influence information is realized. Therefore, the game can be progressed based on the influence information.

[0160] In an aspect in which a computer further functions as the selection determination means, a competitive game in which a plurality of players compete against each other is provided as the game, and the predetermined process is configured to output, as the influence information, information regarding the relative merits of the influence that each selection set has on victory in the competitive game, and the series of selections of the targets may be executed as the selections of one of the plurality of players. In this case, an execution target is determined based on the influence that each selection set has on victory, and the execution target is executed as the selection of one player. Thus, a so-called computer battle can be realized that utilizes the influence that each selection set has on victory.

[0161] The multiple players may be any suitable players. For example, the multiple players may include users (people), or may not include users and may all be computers (not limited to a computer executing the computer program of the present invention, but including another computer that uses the decision results provided by that computer). Similarly, a competitive game may be configured as appropriate. For example, a competitive game may be configured so that multiple players make choices simultaneously (in parallel), or so that turns are given in sequence, each including a series of selection opportunities. Even when turns are given in sequence, the choices of players outside of their turn may not be completely excluded in each turn, for example, a selection made by one player in their turn may prompt another player to make a selection. Furthermore, when turns are given in sequence, the change of turns may be automatically performed according to time, the number of instructions, etc., or may be performed as one of the choices in each turn. Furthermore, each turn may be configured as appropriate, for example, it may include only a series of selection opportunities, or it may include other selection opportunities. Each turn may include various selection opportunities that are appropriately differentiated through phases, etc., and the options at each selection opportunity may all be the same, or at least some (including all) of the options may be different. The end of each phase may be automatic or optional. The choice to change turns or end a phase may function as one of a series of choices, or may be performed as a separate choice from the series of choices.

[0162] For example, in one aspect of the computer program of the present invention, the competitive game may be configured to provide turns including the series of selection opportunities to each player in turn. Furthermore, in this aspect, the competitive game may be configured to provide a player other than the first player with a counter selection opportunity as a selection opportunity to make a selection opposing the first player before at least one selection in the target series of selections is made, and the selection determination means may determine subsequent selections in accordance with a predetermined rule if the selection in the counter selection opportunity makes the first selection impossible to make. In this case, even if a series of selections becomes impossible to make due to the counter selection opportunity, an executable selection is determined in place of the impossible selection in accordance with the predetermined rule. In other words, because the series of selections is complemented by the determination of an alternative selection, the counter selection opportunity does not prevent the acquisition of influence information in units of selection sets. Therefore, even in a game including a counter selection opportunity, it is possible to obtain influence information in units of selection sets.

[0163] When a selection after one selection in a series of target selections is determined according to a predetermined rule, the predetermined rule may be set appropriately. For example, the predetermined rule may be set to a rule that uses a selection set having the next most favorable influence information of the target based on influence information at the time of determining the selection set of the target. Alternatively, the predetermined rule may be set to a rule that randomly determines a selection from feasible selection candidates. Specifically, for example, in one aspect of the present invention that uses a predetermined rule, the predetermined rule may be a lottery result, and the selection determination means may randomly determine selections after the first selection according to the lottery result.

[0164] A series of choice opportunities may be composed of appropriate choice opportunities that are planned to be provided as a set. For example, the series of choice opportunities may be choice opportunities for performing related choices, or may be choices that are unrelated to each other but require selection as a set. Furthermore, when composed of related choice opportunities, the relationship may be appropriate. For example, the relationship may be of a type in which a choice in an earlier choice opportunity limits (changes) the selection candidates in a later choice opportunity. Alternatively, the relationship may be of a type in which a choice in any choice opportunity, including the first choice opportunity, derives the selection candidates in a later choice opportunity. For example, in one aspect of the computer program of the present invention, the series of choice opportunities may be configured so that choices derived from the selection in one choice opportunity are provided in choice opportunities following a previous choice opportunity. In this case, more appropriate influence information can be obtained for choices that tend to be complicated and derive from the selection in one choice opportunity.

[0165] The predetermined process may be configured as appropriate as long as it can output impact information. For example, the predetermined process may be configured to output impact information according to 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 output impact information. Specifically, in one aspect of the computer program of the present invention, the predetermined process may be configured as a trained artificial intelligence model (23) generated to output the impact information by machine learning a predetermined training dataset into a pre-trained model.

[0166] On the other hand, the game system of the present invention is a game system (2) that provides a game that progresses according to the results of selections made in a series of selection opportunities, and is equipped with an information acquisition means (25) that acquires game information indicating the state of the game necessary to obtain impact information regarding the impact that each selection set has on the game, with a combination of selections made in the series of selection opportunities being treated as one selection set, and an impact acquisition means (25) that acquires the impact information regarding each selection set using a predetermined process (23) that outputs the impact information based on the game information.

[0167] Furthermore, the control method of the present invention causes a computer (21) incorporated in a game system (2) that provides a game that progresses according to the results of selections made in a series of selection opportunities to execute an information acquisition procedure for acquiring game information indicating the state of the game necessary to obtain influence information regarding the influence that each selection set has on the game, with a combination of a series of selections made in the series of selection opportunities being treated as one selection set, and an influence acquisition procedure for acquiring the influence information regarding each selection set using a predetermined process that outputs the influence information based on the game information. [Explanation of symbols]

[0168] 2 Game Server (Game System) 21 Control unit (computer) 23 Analysis model section (prescribed processing) 25 COM battle realization unit (information acquisition means, influence acquisition means, selection decision means) PG1 Server program (computer program)

Claims

1. A computer incorporated in a game system that provides a game including a progression according to the results of selections made in a series of selection opportunities, an information acquisition means for acquiring game information indicating a situation of the game necessary to obtain influence information regarding the influence that each combination of a series of selections in the series of selection opportunities has on the game, with each combination being treated as one selection set; an influence acquisition means for acquiring the influence information regarding each selection set by utilizing a predetermined process for outputting the influence information based on the game information; A computer program configured to function as

2. The computer 2. The computer program according to claim 1, further functioning as a selection determination means for determining one of the plurality of selection sets as a selection set to be executed based on the influence information, so that a series of target selections corresponding to a selection set to be executed among the plurality of selection sets is executed in the series of selection opportunities.

3. As the game, a competitive game in which a plurality of players compete against each other is provided, the predetermined process is configured to output, as the influence information, information regarding the relative merits of the influence that each selection set has on winning the competitive game; The computer program product of claim 2 , wherein the series of target selections is executed as selections of one player of the plurality of players.

4. The computer program product according to claim 3 , wherein the competitive game is configured such that turns including the series of selection opportunities are provided to each player in turn.

5. the competitive game is configured to provide a player other than the one player with a counter selection opportunity as a selection opportunity to make a selection against the one player before at least one selection of the target series of selections is made; 5. The computer program according to claim 4, wherein the selection determination means, when the one selection cannot be made due to the selection result of the competing selection opportunity, determines the selections after the one selection in accordance with a predetermined rule.

6. The predetermined rule is a result of a lottery, 6. The computer program according to claim 5, wherein the selection determination means randomly determines the selections after the first selection in accordance with the lottery result.

7. The computer program according to any one of claims 1 to 6, wherein the series of selection opportunities is configured such that in a selection opportunity following a selection opportunity, an option derived from a selection made in the selection opportunity is provided.

8. The computer program according to any one of claims 1 to 6, wherein the predetermined processing is configured as a trained artificial intelligence model generated to output the impact information by machine learning a predetermined training dataset into a pre-training model.

9. A game system that provides a game that progresses according to selection results in a series of selection opportunities, an information acquisition means for acquiring game information indicating a state of the game necessary to obtain influence information regarding the influence that each combination of a series of selections in the series of selection opportunities has on the game, with the combination being one selection set; an influence acquisition means for acquiring the influence information for each selection set by utilizing a predetermined process for outputting the influence information based on the game information; A game system comprising:

10. A computer incorporated in a game system that provides a game including progression according to selection results in a series of selection opportunities, an information acquisition step of acquiring game information indicating a situation of the game necessary to obtain influence information regarding the influence that each combination of a series of selections in the series of selection opportunities has on the game, with each combination being treated as one selection set; an influence acquisition step of acquiring the influence information for each selection set by utilizing a predetermined process for outputting the influence information based on the game information; A control method for executing the above.

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