Game player model utilization providing device

Abstract

An apparatus 700 making a game player model available for use comprises: a reception means 721 that receives a usage instruction for enabling the use of a game player model in which an action to be learned in a second play state has been learned on the basis of play data and a label indicating whether something is an object of learning, the play data including a first play state of a game and actions taken by players in the first play state; and an output means 722 that outputs, in response to the usage instruction, model information for using the game player model.

Description

[Technical field]

[0001] The present invention relates to a game player model utilization providing device, a game player model utilization providing method, and a recording medium. [Background technology]

[0002] 2. Description of the Related Art In various games, such as board games, such as Go and Shogi, and computer games, such as fighting games and shooting games, a computer may control characters.

[0003] One of the techniques used for such computer control is, for example, Patent Document 1. Patent Document 1 describes a fighting game learning device that includes a storage unit for storing various programs and data, and a control unit for controlling the movements of multiple characters appearing in the fighting game based on the operation state of the input operation unit and the program stored in the storage unit. According to Patent Document 1, the control unit collects operation data related to the technique performed by the character in response to the operation of the input operation unit at each predetermined timing and screen state data related to the screen display, and executes the learning program to write the screen state data collected at each predetermined timing into the learning data storage unit. Then, the control unit optimizes the weight of the learning result by performing a calculation process of deep learning based on the screen state data stored in the learning data storage unit. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] JP 2019-195512 A [Non-patent literature]

[0005] [Non-Patent Document 1] Wang Lu, 8 others, “Adversarial Cooperative Imitation Learning for Dynamic Treatment Regimes”, [online], April 20 - 24, 2020, Proceedings of The Web Conference 2020, [searched on September 8, Reiwa 3], Internet <URL:https: / / dl.acm.org / doi / 10.1145 / 3366423.<3380248>

Summary of the Invention

Problems to be Solved by the Invention

[0006] In order to perform appropriate learning according to the learning target, such as more human-like, it is desirable to perform imitation learning as described in Patent Document 1 rather than reinforcement learning in which the learner learns through its own actions. However, in order to perform imitation learning appropriately, a large amount of history such as play data is required. However, when simply collecting play data according to the player's operation as described in Patent Document 1, it is difficult to collect sufficient play data required for imitation learning. As a result, there has been a problem that it is difficult to provide a model that performs learning according to the learning target, such as the operation of a computer player close to human operation.

[0007] Therefore, an object of the present invention is to provide a server device, a model providing method, and a recording medium that can solve the problem that it is difficult to provide a model that performs learning according to the learning target, such as the operation of a computer player close to human operation.

Means for Solving the Problems

[0008] In order to achieve such an object, a game player model utilization providing device according to one embodiment of the present disclosure is a receiving means for receiving a usage instruction for enabling the use of a game player model that has learned the behavior of the learning subject in a second play state based on play data including a first play state in a game and an action taken by a player in the first play state and a label indicating whether or not the learning subject is a learning subject; an output means for outputting model information for using the game player model in response to the usage instruction; Equipped The structure is as follows.

[0009] In addition, a method for providing a game player model according to another embodiment of the present disclosure includes: An information processing device, receiving a usage instruction for enabling use of a game player model that has learned the behavior of the learning subject in a second play state based on play data including a first play state in a game and an action taken by a player in the first play state and a label indicating whether the learning subject is a learning subject; In response to the usage instruction, model information for using the game player model is output. The structure is as follows.

[0010] In addition, a recording medium according to another embodiment of the present disclosure includes: In the information processing device, receiving a usage instruction for enabling use of a game player model that has learned the behavior of the learning subject in a second play state based on play data including a first play state in a game and an action taken by a player in the first play state and a label indicating whether the learning subject is a learning subject; In response to the usage instruction, model information for using the game player model is output. It is a computer-readable recording medium on which a program for implementing processing is recorded. Effect of the Invention

[0011] According to the configurations described above, it is possible to provide a game player model utilization providing device, a game player model utilization providing method, and a recording medium that are capable of providing a model that has been suitably trained to bring the operation of a computer player closer to that of a human, and thus closer to the subject of study. [Brief description of the drawings]

[0012] [Figure 1] FIG. 2 is a diagram for explaining a learning device according to the first embodiment of the present disclosure. [Diagram 2] FIG. 2 is a block diagram showing a configuration example of a learning device. [Diagram 3] FIG. 3 is a diagram showing an example of input data shown in FIG. 2; [Figure 4] FIG. 11 is a diagram illustrating an example of an attribute. [Diagram 5] FIG. 13 is a diagram showing an example of play data to be collected. [Figure 6] FIG. 13 is a diagram showing another example of play data. [Figure 7] FIG. 11 is a diagram for explaining another example of play data. [Figure 8] FIG. 11 is a diagram for explaining an example of a learning process. [Figure 9] 13 is a flowchart showing an example of the operation of the learning device. [Figure 10] FIG. 13 is a block diagram showing another example of the configuration of the learning device. [Figure 11] FIG. 4 is a diagram for explaining an example of voice information. [Figure 12] FIG. 11 is a diagram illustrating a configuration example of a learning system according to a second embodiment of the present disclosure. [Figure 13] 11 is a block diagram showing an example of the configuration of the customer terminal shown in FIG. 10. [Figure 14] 11 is a block diagram showing an example of the configuration of a server device shown in FIG. [Figure 15] FIG. 11 is a diagram illustrating an example of a billing process. [Figure 16] FIG. 11 is a diagram for explaining another example of the billing process. [Figure 17]10 is a flowchart illustrating an example of the operation of the server device. [Figure 18] 10 is a flowchart illustrating an example of the operation of the server device. [Figure 19] FIG. 13 is a block diagram showing an example of a hardware configuration of a game play operation learning device according to a third embodiment of the present disclosure. [Figure 20] 1 is a block diagram showing an example of the configuration of a game play operation learning device; [Figure 21] FIG. 13 is a block diagram showing an example configuration of a game player model utilization and provision device according to a fourth embodiment of the present disclosure. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0013] [First embodiment] A first embodiment of the present disclosure will be described with reference to Figs. 1 to 11. Fig. 1 is a diagram for explaining a learning device 100. Fig. 2 is a block diagram showing an example of the configuration of the learning device 100. Fig. 3 is a diagram showing an example of input data 121 shown in Fig. 2. Fig. 4 is a diagram for explaining an example of attributes. Figs. 5 and 6 are diagrams showing an example of play data to be collected. Fig. 7 is a diagram for explaining another example of play data. Fig. 8 is a diagram for explaining an example of a learning process. Fig. 9 is a flowchart showing an example of the operation of the learning device 100. Fig. 10 is a block diagram showing another example of the configuration of the learning device 100. Fig. 11 is a diagram for explaining an example of voice information.

[0014] In the first embodiment of the present disclosure, a learning device 100 (game play operation learning device) that performs machine learning based on play data of various games such as board games such as Go and Shogi, and computer games such as fighting games and shooting games will be described. As shown in FIG. 1, the learning device 100 in this embodiment generates a game player model for outputting the behavior of a learning target in response to an input of a play state, based on play data to which a label indicating whether or not the learning target is attached is attached. That is, the learning device 100 performs machine learning using both play data to which a label indicating that the learning target is attached is attached and play data to which a label indicating that the learning target is not attached is attached. Specifically, for example, a first label, which is a label of a success case, is attached to play data having an attribute to be learned, and a second label, which is a label of a failure case, which is different from the first label, is attached to play data having an attribute different from the learning target. Then, the learning device 100 performs machine learning so as to approach play data having an attribute to be learned and to move away from play data having an attribute different from the learning target.

[0015] The learning device 100 is an information processing device that performs machine learning based on game play data acquired from an external device or the like. Games may include board games such as Go and Shogi, computer games such as fighting games and shooting games, and any other games. For example, the learning device 100 is a server device or the like. The learning device 100 may be a single information processing device, or may be realized, for example, on the cloud.

[0016] Fig. 2 shows an example of the configuration of the learning device 100. Referring to Fig. 2, the learning device 100 has, as main components, for example, a communication I / F unit 110, a storage unit 120, and a calculation processing unit 130.

[0017] The communication I / F unit 110 is composed of a data communication circuit, etc. The communication I / F unit 110 performs data communication with an external device, etc., connected via a communication line.

[0018] The storage unit 120 is a storage device such as a hard disk or a memory. The storage unit 120 stores processing information and a program 123 required for various processes in the arithmetic processing unit 130. The program 123 is read into the arithmetic processing unit 130 and executed to realize various processing units. The program 123 is read in advance from an external device or a recording medium via a data input / output function such as the communication I / F unit 110, and is stored in the storage unit 120. Main information stored in the storage unit 120 includes, for example, input data 121, a neural network 122, and the like.

[0019] The input data 121 includes play data indicating actions taken by the player in the game, the state of the game, etc. The input data 121 is acquired for learning purposes from an external device or the like via the communication I / F unit 110 or the like.

[0020] Fig. 3 shows an example of the input data 121. As shown in Fig. 3, the input data 121 includes play data with a predetermined attribute to which a label of a success example has been added, and play data with an attribute different from the above-mentioned predetermined attribute to which a label of a failure example has been added. For example, the input data 121 includes a plurality of play data with a label of a success example added and a plurality of play data with a label of a failure example added.

[0021] Here, the attribute indicates information corresponding to a player, such as the type and skill level of a player playing a game, the characteristics of the player, and the like. FIG. 4 shows an example of the attribute. Referring to FIG. 4, the attribute may include, for example, a player attribute, a skill level attribute, a person attribute, and a specific person attribute. Specifically, for example, the player attribute indicates the type of player, such as whether the player is a human or an AI (artificial intelligence). Furthermore, the skill level attribute indicates the player's skill in the game, such as advanced player, intermediate player, beginner, xx-dan, professional, and the like. Furthermore, the person attribute indicates information corresponding to a player, such as address and gender. Furthermore, the specific person attribute indicates a specific person or individual, such as professional A, YouTuber B, and the like. The specific person attribute may be, for example, an identifier uniquely given to an individual.

[0022] The play data has attributes according to the characteristics of the player who plays the game as exemplified above. Note that the attributes may be based on the characteristics of the play data, such as the frequency of a particular action within a given period of time, instead of or in addition to the player's characteristics.

[0023] Also, a label indicating whether or not the play data is a learning target is given to the play data in advance, for example, in an external device. Specifically, for example, a first label, a label of a success example, is given to play data having an attribute to be learned, and a second label, a label of a failure example, different from the first label, is given to play data having an attribute different from the attribute to be learned. A failure example label may be given to play data having an attribute opposite to the attribute to be learned, rather than simply a different attribute.

[0024] As an example, when a label of a success example is given to the play data of an advanced player, a label of a failure example is given to the play data having attributes different from those of the advanced player, such as an intermediate player or a beginner. Also, when a label of a success example is given to the play data of an advanced player, a label of a failure example may be given to the play data having attributes opposite to those of the advanced player, such as a beginner. Also, when a label of a success example is given to the play data having a specific person attribute indicating a specific person, such as a professional A, a label of a failure example may be given to the play data not having a specific person attribute indicating a specific person. Note that, in each of the above examples, a label of a failure example may be given to the play data having a player attribute indicating that the player is an AI that is not a human. By giving a label of a failure example to the play data having a player attribute indicating that it is an AI, it is possible to perform machine learning processing so as to move away from AI-like, that is, non-human, behavior. For example, by labeling play data having a specific person attribute indicating a specific person as a success example, and labeling play data having a player attribute indicating an AI as a failure example, it becomes possible to update the weight values ​​of the neural network 122 so as to approach the play data of a specific person and to move away from unhuman behavior.

[0025] In this embodiment, the attributes to be learned may be specified by any means. Instead of assigning a label in advance, the learning device 100 may be configured to assign a label to the play data based on information indicating the attribute acquired together with the play data or information indicating the attribute to be learned.

[0026] As described above, the play data included in the input data 121 indicates actions taken by the player in the game, game states, etc. For example, the play data includes state information indicating the game state (first play state) in the game, action information indicating actions taken by the player in the above state, etc.

[0027] As an example, FIG. 5 shows an example of play data when a fighting game is played as a game. Referring to FIG. 5, the play data includes information for each target, which is a character that will fight. The state information includes at least one of the following: identification information for identifying the character that will fight, character information indicating the remaining vitality of the character, position information indicating the position coordinates indicating the position of the character, orientation information indicating the orientation, movement speed indicating the speed at which the character moves, and movement information indicating the movement of the character during action. The movement information includes key information indicating the key input by the player. The play data may include information other than the above examples.

[0028] As another example, FIG. 6 shows an example of play data when a game of shogi is played. Referring to FIG. 6, the play data includes information for two players playing shogi. The state information includes at least one of piece position information indicating the position of a piece, held piece information indicating the type of held piece, remaining time information indicating the remaining time, and the like. The action information includes at least one of piece type information indicating the type of moved piece, previous position information indicating the position of the moved piece before it was moved, subsequent position information indicating the position of the moved piece, consumed time information indicating the time consumed before the piece was moved, and the like. The play data may include information other than the above examples.

[0029] In this way, the input data 121 includes play data corresponding to the game to be learned. The input data 121 may include play data for each scene individually, or may include play data as time-series data in which states and actions are linked as shown in Fig. 7. For example, the play data may include a first play state in the game, an action in the first play state, and a third play state resulting from the action. By learning from time-series data, it is possible to adjust weight values ​​and the like to enable more appropriate output.

[0030] The neural network 122 is subjected to machine learning processing using the input data 121, which is teacher data, etc., so that when play data including state information is input, the neural network 122 outputs action information corresponding to the state information, etc. In other words, the neural network 122 is subjected to machine learning processing so as to output a behavior to be learned in response to an input of a second play state.

[0031] The arithmetic processing unit 130 has an arithmetic device such as a CPU (Central Processing Unit) and its peripheral circuits. The arithmetic processing unit 130 reads and executes a program 123 from the storage unit 120, thereby realizing various processing units by making the above hardware and the program 123 work together. Main processing units realized by the arithmetic processing unit 130 include, for example, an acquisition unit 131, a learning unit 132, and an output unit 133.

[0032] The acquisition unit 131 acquires play data and the like from an external device, etc. For example, the acquisition unit 131 acquires information indicating attributes of the play data along with the play data. The acquisition unit 131 also stores the acquired play data and the like in the storage unit 120 as input data 121.

[0033] Furthermore, the acquisition unit 131 can acquire information indicating attributes to be learned, etc. For example, the acquisition unit 131 may acquire information indicating attributes to be learned together with play data, or may acquire information indicating attributes to be learned at a timing different from that of the play data.

[0034] The learning unit 132 performs machine learning to output a learning target behavior in response to an input of a second play state, based on play data including a first play state and an action, and input data 121 including a label. For example, the learning unit 132 inputs the input data 121, which is training data, to the neural network 122. Then, the learning unit 132 updates weight values ​​of the neural network 122 so as to approach play data labeled as a success case and to move away from play data labeled as a failure case. For example, the learning unit 132 repeats the above process using a large amount of training data to generate a game player model, which is a created model corresponding to the attribute to be learned. Note that the learning unit 132 may perform machine learning processing using a known means.

[0035] As an example, as shown in FIG. 8, the learning unit 132 may train the AI ​​that imitates a success case by making it compete with the AI ​​that distinguishes successful actual cases and cooperate with the AI ​​that distinguishes unsuccessful actual cases, such as updating the weight value. Here, the AI ​​that imitates a success case may be adjusted by, for example, performing imitation learning based on play data that is labeled with a success case included in the input data 121. The AI ​​that distinguishes successful actual cases may be adjusted by performing machine learning so as to distinguish successful cases based on play data that is labeled with a success case included in the input data 121. The AI ​​that distinguishes unsuccessful actual cases may be adjusted by performing machine learning so as to distinguish unsuccessful cases based on play data that is labeled with a failure case included in the input data 121. As shown in FIG. 8, the learning unit 132 may adjust each AI by providing each AI with feedback based on the results of making the AI ​​that distinguishes successful cases and the AI ​​that distinguishes unsuccessful cases distinguish play data generated by the AI ​​that imitates successful cases. For example, as described above, the learning unit 132 may be configured to perform machine learning based on the input data 121, which is teacher data, by performing imitation learning of an adversarial and collaborative generation method using a neural network, as one example. Specifically, for example, the learning unit 132 may perform machine learning processing using a method such as that described in Non-Patent Document 1. Note that the learning method by the learning unit 132 is not limited to the above-mentioned example. The learning unit 132 may perform machine learning based on the input data 121 using a known method other than the above-mentioned example.

[0036] The learning unit 132 may be configured to generate teacher data by labeling the play data based on the information indicating the attribute to be learned acquired by the acquisition unit 131. For example, the learning unit 132 may label the play data having the attribute to be learned as a success example, while labeling the play data having an attribute different from the attribute to be learned as a failure example. The learning unit 132 may label the play data having an attribute opposite to the attribute to be learned, among the play data having an attribute different from the attribute to be learned, as a failure example. Note that which attributes are opposite to each other may be determined in advance, for example, or may be determined by the learning unit 132 by any means.

[0037] The output unit 133 outputs a game player model or the like that is a result of learning by the learning unit 132. For example, the output unit 133 can output the game player model or the like to an external device or the like via the communication I / F unit 110 or the like.

[0038] The above is an example of the configuration of learning device 100. Next, an example of the operation of learning device 100 will be described with reference to FIG.

[0039] FIG. 9 shows an example of the operation of the learning device 100. Referring to FIG. 9, the acquisition unit 131 The acquisition unit 131 acquires play data and the like from an external device or the like (step S101). The acquisition unit 131 also stores the acquired play data and the like in the storage unit 120 as input data 121.

[0040] The learning unit 132 inputs the input data 121, which is teacher data, to the neural network 122. Then, the learning unit 132 updates weight values ​​and the like of the neural network 122 so that the play data approaches play data labeled as a success example and moves away from play data labeled as a failure example. For example, the learning unit 132 performs machine learning processing based on the input data 121 as described above (step S102). Note that the processing of step S101 and the processing of step S102 do not necessarily have to be consecutive.

[0041] In this way, the learning device 100 has a learning unit 132. With this configuration, the learning unit 132 can perform machine learning processing based on the input data 121 including play data having a specific attribute to be learned and play data having an attribute different from the learning target. In other words, the machine learning processing can be performed using both play data to which a label indicating that it is a learning target is attached and play data to which a label indicating that it is not a learning target is attached. As a result, machine learning based on a larger amount of play data can be performed compared to the case where machine learning is simply performed based on play data having a specific attribute to be learned. This makes it possible to appropriately perform learning to approach the learning target even in cases where it is difficult to collect a sufficient amount of play data having a specific attribute.

[0042] The configuration of the learning device 100 is not limited to the example shown in Fig. 2. For example, Fig. 10 shows another example configuration of the learning device 100. Referring to Fig. 10, the calculation processing unit 130 of the learning device 100 executes the program 123, thereby realizing a voice information acquisition unit 134 in addition to the configuration shown in Fig. 2.

[0043] The voice information acquisition unit 134 acquires voice information indicating the voice of a specific person. Then, the voice information acquisition unit 134 stores the acquired voice information in the storage unit 120 as voice information 124. For example, when the acquisition unit 131 acquires play data, the voice information acquisition unit 134 acquires information indicating a voice having a specific person attribute similar to the play data. When acquiring information indicating an attribute to be learned at a timing different from that of the play data, the voice information acquisition unit 134 may acquire information indicating a voice at the timing of acquiring information indicating an attribute to be learned.

[0044] Fig. 11 shows an example of the voice information 124. Referring to Fig. 11, in the voice information 124, output status information indicating a status for outputting voice is associated with voice data for each attribute such as a specific person attribute. For example, in the case of Fig. 11, the voice data "Please take your time" is associated with the status of "thinking for a long time."

[0045] In the case where the storage unit 120 includes the audio information 124, when the output unit 133 outputs a game player model or the like that is the result of learning by the learning unit 132, the output unit 133 can output the audio information 124 corresponding to the learning target together with the game player model or the like. This allows an external device or the like that receives the audio information 124 to utilize the results of learning by the learning unit 132 and to output audio based on the audio information 124. As a result, it becomes possible to provide a communication experience in an external device or the like, as if the user were playing with a player imitated by an AI.

[0046] [Second embodiment] Next, a second embodiment of the present invention will be described with reference to Fig. 12 to Fig. 18. Fig. 12 is a diagram showing an example of the configuration of a learning system 200. Fig. 13 is a block diagram showing an example of the configuration of a customer terminal 300. Fig. 14 is a block diagram showing an example of the configuration of a server device 400. Figs. 15 and 16 are diagrams for explaining an example of a billing process. Figs. 17 and 18 are flowcharts showing an example of the operation of the server device.

[0047] In a second embodiment of the present disclosure, a learning system 200 including a learning device 500 having similar functions to the learning device 100 described in the first embodiment will be described. As will be described later, in this embodiment, the learning device 500 performs machine learning using a method similar to that of the learning device 100 described in the first embodiment, so as to approximate the play data of a specific person, such as a professional e-sports player, a YouTuber, or a celebrity.

[0048] Fig. 12 shows an example of the configuration of the learning system 200. Referring to Fig. 12, the learning system 200 has a plurality of customer terminals 300, a server device 400 which is a game player model utilization providing device, and a learning device 500. As shown in Fig. 12, the customer terminals 300 and the server device 400 are connected to each other so as to be able to communicate with each other via a network or the like. In addition, the server device 400 and the learning device 500 are connected to each other so as to be able to communicate with each other via a network or the like.

[0049] The customer terminal 300 is an information processing device on which a player plays a game. For example, the customer terminal 300 may be any information processing device such as a video game device for executing a video game, a personal computer, or a tablet terminal.

[0050] Fig. 13 shows the configuration of a customer terminal 300 characteristic of this embodiment. Referring to Fig. 13, the customer terminal 300 has a play data acquisition unit 310, a transmission unit 320, and a usage instruction unit 330 in addition to components necessary for executing a game. For example, the customer terminal 300 has a calculation device such as a CPU and a storage device. For example, the customer terminal 300 can realize each of the above processing units by causing the calculation device to execute a program stored in the storage device.

[0051] The play data acquisition unit 310 acquires play data indicating actions taken by the player in the game and the state of the game when the player plays the game. The play data acquisition unit 310 may acquire play data at predetermined intervals, or may acquire play data when a predetermined condition is satisfied, such as when the player performs an action. The play data acquisition unit 310 may also acquire play data as time-series data in which states and actions are linked together. The play data acquired by the play data acquisition unit 310 may be stored in a storage device possessed by the customer terminal 300.

[0052] The transmitting unit 320 transmits the play data acquired by the play data acquiring unit 310 to the server device 400. The transmitting unit 320 may transmit, together with the play data, for example, information indicating the attributes of the player stored in advance in the customer terminal 300 to the server device 400. For example, the transmitting unit 320 can transmit the play data and the like to the server device 400 at any timing.

[0053] The use instruction unit 330 instructs the server device 400 to make available a game player model, etc., according to the learning result corresponding to a specific person attribute indicating a specific person. In other words, the use instruction unit 330 transmits to the server device 400 a use instruction indicating a request for transmission of model information, etc., required to make the game player model available in the customer terminal 300. For example, the use instruction unit 330 instructs the server device 400, in response to an input from a player operating the customer terminal 300, to make available a game player model, etc., indicated by the input from the player.

[0054] The server device 400 is an information processing device that accumulates play data and accumulates a game player model. In addition, the server device 400 receives a learning instruction and instructs the learning device 500 to perform learning corresponding to a specific person attribute indicating a specific person, and transmits a game player model or model information for using the game player model to the customer terminal 300 in response to an instruction from the customer terminal 300. The server device 400 may be a single information processing device, or may be realized, for example, on a cloud.

[0055] Fig. 14 shows an example of the configuration of the server device 400. Referring to Fig. 14, the server device 400 has, as main components, for example, a communication I / F unit 410, a storage unit 420, and a calculation processing unit 430.

[0056] The communication I / F unit 410 is composed of a data communication circuit, etc. The communication I / F unit 410 performs data communication with an external device, etc., connected via a communication line.

[0057] The storage unit 420 is a storage device such as a hard disk or a memory. The storage unit 420 stores processing information and a program 423 required for various processes in the calculation processing unit 430. The program 423 is read into the calculation processing unit 430 and executed to realize various processing units. The program 423 is read in advance from an external device or a recording medium via a data input / output function such as the communication I / F unit 410, and is stored in the storage unit 420. Main information stored in the storage unit 420 includes, for example, play data information 421 and created model information 422. The storage unit 420 may also include information equivalent to the voice information 124 described in the first embodiment.

[0058] The play data information 421 includes the play data received from the customer terminal 300. For example, the play data information 421 stores play data and attributes corresponding to the play data in association with each other. The details of the play data and attributes may be the same as those in the first embodiment.

[0059] The created model information 422 includes a game player model, which is a created model created by performing machine learning processing in the learning device 500. For example, in the created model information 422, the game player model is associated with information indicating the attributes that were the learning target when the game player model was created.

[0060] The arithmetic processing unit 430 has an arithmetic device such as a CPU and its peripheral circuits. The arithmetic processing unit 430 reads and executes the program 423 from the storage unit 420, thereby implementing various processing units through cooperation between the above hardware and the program 423. Major processing units implemented by the arithmetic processing unit 430 include, for example, a play data receiving unit 431, a creation instruction transmitting / receiving unit 432, a created model receiving unit 433, a usage instruction accepting unit 434, an output unit 435, and a billing unit 436.

[0061] The play data receiving unit 431 receives play data and information indicating attributes from the customer terminal 300. The play data receiving unit 431 also stores the received information in the storage unit 420 as play data information 421.

[0062] The creation instruction transmitting / receiving unit 432 receives an instruction to create a game player model from an external device such as the customer terminal 300. For example, the creation instruction transmitting / receiving unit 432 receives an instruction to create a game player model together with a specific person attribute that is an attribute to be learned.

[0063] Furthermore, upon receiving the creation instruction, the creation instruction transmitting / receiving unit 432 refers to the play data information 421 to identify play data having a specific person attribute to be studied. Furthermore, the creation instruction transmitting / receiving unit 432 refers to the play data information 421 to identify play data to which a failure example label is to be added. As described in the first embodiment, the play data to which a failure example label is to be added may be play data having attributes opposite to those of the play data to which a success example is to be added. Then, the creation instruction transmitting / receiving unit 432 transmits the identified play data and an instruction to create a game player model to the learning device 500.

[0064] The labeling of success cases and failure cases may be performed by the creation instruction transmitting / receiving unit 432 or by the learning device 500. The play data may be transmitted in advance to the learning device 500. In this case, the creation instruction transmitting / receiving unit 432 may omit the process of identifying and transmitting the play data.

[0065] The created model receiving unit 433 receives from the learning device 500 a game player model, which is a created model created in response to the creation instruction transmitted by the creation instruction transmitting / receiving unit 432. In other words, the created model receiving unit 433 receives from the learning device 500 a game player model created based on play data having an attribute to be learned and play data having an attribute different from the attribute to be learned. For example, the created model receiving unit 433 receives a game player model and information indicating the attribute that was the learning target when the game player model was created. In addition, the created model receiving unit 433 stores the received various information in the storage unit 420 as created model information 422.

[0066] The usage instruction receiving unit 434 receives a usage instruction from the customer terminal 300 .

[0067] When the use instruction receiving unit 434 receives a use instruction from the customer terminal 300, the output unit 435 refers to the created model information 422 to identify a game player model corresponding to the use instruction. Then, the output unit 435 transmits model information and the like required to use the identified game player model to the customer terminal 300. That is, the output unit 435 transmits to the customer terminal 300 model information required to use the game player model, which is a created model created based on play data having an attribute to be learned and play data having an attribute different from the learning target. The model information may be the game player model itself, or may be permission information for allowing the customer terminal 300 to use the game player model by accessing the server device 400 or the like. The permission information may be limited to a certain period of time. The output unit 435 may be configured to transmit voice information 124 having a matching attribute together with the game player model or to allow the voice information 124 to be used.

[0068] The billing unit 436 performs billing processing for the customer terminal 300 and the like.

[0069] FIG. 15 shows an example of a charging process by the charging unit 436. Referring to FIG. 15, for example, when the charging unit 436 receives an instruction to create a game player model from an external device such as the customer terminal 300, which is the target of creation, the charging unit 436 can request a registration fee from the external device that is the source of the creation instruction. For example, the creation instruction transmission / reception unit 432 can be configured to transmit an instruction to create a game player model, etc. to the learning device 500 on condition that the charging unit 436 receives the registration fee. The registration fee may be, for example, a predetermined amount. In addition, when the output unit 435 transmits model information, etc. to the customer terminal 300 in response to a usage instruction from the customer terminal 300, the charging unit 436 can request a model usage fee from the customer terminal 300. That is, the charging unit 436 can request a model usage fee from the customer terminal 300 that uses the game player model. For example, the output unit 435 can be configured to transmit model information, etc. to the customer terminal 300 on condition that the charging unit 436 receives the model usage fee. The model usage fee may be, for example, a predetermined amount.

[0070] The billing unit 436 can be configured to pay a model usage fee to an external device, such as the customer terminal 300 that sent the instruction to create a game player model, depending on the number of available game player models. For example, the billing unit 436 may be configured to check the number of available game player models at a predetermined interval to check whether or not the model usage fee has been paid. The model usage fee may be variable, for example, up to a predetermined upper limit, so that the more the number of uses of the game player model increases, the higher the fee becomes.

[0071] Furthermore, the billing unit 436 can pay a model provision fee to the learning device 500 when the created model receiving unit 433 receives a game player model from the learning device 500, or when the creation instruction transmitting / receiving unit 432 transmits an instruction to create a game player model to the learning device 500. The model provision fee may be, for example, a predetermined amount. The billing unit 436 may pay an additional usage fee to the learning device 500 according to the number of available game player models, the number of instructions to create a game player model, etc. The additional usage fee may be, for example, variable so that it increases as the number of available game player models, the number of instructions to create a game player model, etc. increases.

[0072] As shown in FIG. 16, the charging unit 436 may be configured to pay a contract fee to an external device such as the customer terminal 300 instead of or in addition to a registration fee or a model usage fee. For example, the charging unit 436 may be configured to estimate the number of uses of the game player model, and use the process illustrated in FIG. 15 or the process illustrated in FIG. 16 depending on the estimated result, the popularity of the person to be created, and the like. Specifically, for example, when it is determined that a predetermined condition is satisfied, such as when the estimated number of uses is equal to or greater than a predetermined value, or when it is determined that the popularity is equal to or greater than a predetermined value, the charging unit 436 may determine to perform the process illustrated in FIG. 16 instead of the process illustrated in FIG. 15. That is, the charging unit 436 may be configured not to request a registration fee when the external device that is the source of the creation instruction satisfies a predetermined condition, such as when the estimated number of uses is equal to or greater than a predetermined value, or when it is determined that the popularity is equal to or greater than a predetermined value. In other words, the billing unit 436 requests the registration fee only when the external device that is the sender of the creation instruction satisfies a predetermined condition, such as when the estimated number of uses is less than a predetermined value, when the popularity is less than a predetermined value, etc. The popularity may be calculated by any means based on any information, such as the number of subscribers of the channel corresponding to the creation target, the number of views of the video, activity history information such as awards received in competitions, etc., whether or not the creation target has a professional contract, the number of articles in which the creation target appears, and the number of views.

[0073] The above is an example of the configuration of the server device 400. The server device 400 may be connected to a reinforcement learning device that creates an AI by performing reinforcement learning in which a learner learns through his / her own actions. The server device 400 may be configured to receive play data between AIs received from the reinforcement learning device as play data having a player attribute "AI". In this case, the server device 400 may be configured to always identify play data having the player attribute "AI" as play data to which a failure example is assigned.

[0074] The learning device 500 has a similar configuration to the learning device 100 described in the first embodiment. In this embodiment, the learning device 500 mainly performs machine learning to approach play data having specific person attributes. In addition, the learning device 500 performs machine learning to move away from play data having a label of a failure example.

[0075] The above is an example of the configuration of the learning system 200. Next, an example of the operation of the server device 400 will be described with reference to FIG.

[0076] Fig. 17 shows an example of the operation of the server device 400. Referring to Fig. 17, the creation instruction transmitting / receiving unit 432 receives an instruction to create a game player model from an external device such as the customer terminal 300 (step S201). For example, the creation instruction transmitting / receiving unit 432 receives the instruction to create a game player model together with a specific person attribute, which is an attribute to be learned.

[0077] The creation instruction transmitting / receiving unit 432 refers to the play data information 421 to identify play data having a specific person attribute to be studied. The creation instruction transmitting / receiving unit 432 also refers to the play data information 421 to identify play data to which a failure example label is to be added. The creation instruction transmitting / receiving unit 432 then transmits the identified play data and an instruction to create a game player model to the learning device 500 (step S202). Note that the creation instruction transmitting / receiving unit 432 may be configured to transmit the instruction to create a game player model, etc. to the learning device 500 on the condition that the registration fee is received by the billing unit 436.

[0078] The created model receiving unit 433 receives from the learning device 500 a game player model, which is a created model created in response to the creation instruction transmitted by the creation instruction transmitting / receiving unit 432 (step S203). For example, the created model receiving unit 433 receives a game player model and information indicating attributes that were the learning targets when creating the game player model. In addition, the created model receiving unit 433 stores the received various information in the storage unit 420 as created model information 422.

[0079] Also, referring to FIG. 18, the use instruction receiving unit 434 receives a use instruction from the client terminal 300 (step S301, Yes). Then, the output unit 435 refers to the created model information 422 to identify a game player model corresponding to the instruction. Then, the output unit 435 transmits the identified game player model to the client terminal 300 (step S302). Note that the output unit 435 may be configured to transmit audio information 124 having matching attributes together with the game player model. Also, the output unit 435 may be configured to transmit the game player model to the client terminal 300 on the condition that the model usage fee is received by the charging unit 436.

[0080] In this way, the server device 400 is configured to provide a game player model created based on play data having a specific attribute and play data having an attribute different from the above attribute. With this configuration, it is possible to provide customers with a gaming experience that is closer to a specific individual and more natural movements.

[0081] The configuration of the learning system 200 is not limited to the example shown in this embodiment. For example, in this embodiment, the play data is stored in the server device 400. However, the play data may be stored in a device other than the server device 400, such as the learning device 500. In this case, the server device 400 may only output model information without acquiring or storing the play data. Furthermore, the customer terminal 300, the server device 400, etc. may have the function of the learning device 500. In this way, the learning system 200 may employ various modified examples having similar functions as the entire system.

[0082] [Third embodiment] Next, a third embodiment of the present invention will be described with reference to Fig. 19 and Fig. 20. Fig. 19 and Fig. 20 show an example of the configuration of a game play operation learning device 600. In Fig. 19 and Fig. 20, a game play operation learning device 600 is shown.

[0083] The game play operation learning device 600 is an information processing device that performs machine learning processing based on play data to which a label indicating whether or not it is a learning target is attached. Fig. 19 shows an example of the hardware configuration of the game play operation learning device 600. Referring to Fig. 19, the game play operation learning device 600 has, as an example, the following hardware configuration. ·CPU (Central Processing Unit) 601 (computing unit) ROM (Read Only Memory) 602 (storage device) RAM (Random Access Memory) 603 (storage device) · Programs 604 loaded into RAM 603 A storage device 605 for storing the programs 604 A drive device 606 for reading and writing data from a recording medium 610 outside the information processing device A communication interface 607 for connecting to a communication network 611 outside the information processing device Input / output interface 608 for inputting and outputting data Bus 609 connecting each component

[0084] 20. The game play operation learning device 600 can realize the functions of an acquisition means 621, a learning means 622, and an output means 623 shown in Fig. 20 by the CPU 601 acquiring the group of programs 604 and executing them. The group of programs 604 is stored in the storage device 605 or the ROM 602 in advance, for example, and the CPU 601 loads the group of programs 604 into the RAM 603 or the like and executes them as necessary. The group of programs 604 may be supplied to the CPU 601 via the communication network 611, or may be stored in the recording medium 610 in advance, and the drive device 606 may read out the programs and supply them to the CPU 601.

[0085] 19 shows an example of the hardware configuration of the game play operation learning device 600. The hardware configuration of the game play operation learning device 600 is not limited to the above. For example, the game play operation learning device 600 may be configured with a part of the above configuration, such as not having the drive device 606.

[0086] The acquiring means 621 acquires play data including a first play state in the game and an action taken by the player in the first play state, and a label indicating whether or not the action is a learning subject.

[0087] The learning means 622 generates a game player model for outputting a behavior to be learned in response to an input of a second play state, based on the play data and the label.

[0088] The output means 623 outputs the game player model.

[0089] In this way, the game play operation learning device 600 has the learning means 622. With this configuration, the learning means 622 can generate a game player model for outputting the behavior of the learning target in response to the input of the second play state based on the play data and the label. That is, the learning means 622 can perform machine learning processing using both the play data to which a label indicating that it is a learning target is attached and the play data to which a label indicating that it is not a learning target is attached. As a result, the learning means 622 can perform machine learning based on a larger amount of play data compared to the case where machine learning is simply performed based on play data having a specific attribute to be learned. This allows appropriate learning to approach the learning target even in cases where it is difficult to collect a sufficient amount of play data having a specific attribute.

[0090] The game play operation learning device 600 described above can be realized by incorporating a predetermined program into an information processing device such as the game play operation learning device 600. Specifically, a program according to another embodiment of the present invention is a program for implementing a process in an information processing device such as the game play operation learning device 600 to acquire play data including a first play state in a game and an action taken by a player in the first play state, and a label indicating whether or not the action is a learning target, and generate a game player model for outputting the action of the learning target in response to an input of a second play state, based on the play data and the label.

[0091] Furthermore, a game play operation learning method executed by an information processing device such as the above-mentioned game play operation learning device 600 is a method in which the information processing device such as the game play operation learning device 600 acquires play data including a first play state in a game and an action taken by the player in the first play state, and a label indicating whether or not the action is a learning subject, and generates a game player model for outputting the action of the learning subject in response to an input of a second play state, based on the play data and the label.

[0092] Even if the invention is an invention of a program, or a computer-readable recording medium having a program recorded thereon, or a game play operation learning method having the above-mentioned configuration, it has the same action and effect as the above-mentioned game play operation learning device 600, and therefore can achieve the above-mentioned object of the present invention.

[0093] [Fourth embodiment] Next, a fourth embodiment of the present invention will be described with reference to Fig. 21. Fig. 21 shows an example of the configuration of a game player model utilization providing device 700.

[0094] The game player model utilization providing device 700 can have the same hardware configuration as the game play operation learning device 600 described in the third embodiment. In addition, the game player model utilization providing device 700 can realize the functions of the reception means 721 and the output means 722 shown in Fig. 21 by having a CPU acquire and execute a group of programs. Note that the game player model utilization providing device 700 may employ various modified examples, similar to the game play operation learning device 600 described in the third embodiment.

[0095] The receiving means 721 receives a usage instruction from an external device. The usage instruction is an instruction to make available to the external device a game player model that has learned the behavior of the learning target in the second play state. For example, the game player model is learned in advance based on play data including a first play state in the game and the behavior taken by the player in the first play state, and a label indicating whether or not the player is a learning target.

[0096] The output means 722 outputs, in response to the usage instruction received by the reception means 721, model information for using the game player model indicated by the usage instruction.

[0097] In this way, the game player model utilization and provision device 700 has the output means 722. With this configuration, the output means 722 can output a game player model created by machine learning using both play data to which a label indicating that the play data is a learning target and play data to which a label indicating that the play data is not a learning target. As a result, it is possible to provide customers with a gaming experience that is closer to a specific individual, attributes, and more natural movements.

[0098] The above-mentioned game player model utilization providing device 700 can be realized by incorporating a predetermined program into an information processing device such as the game player model utilization providing device 700. Specifically, a program as another embodiment of the present invention is a program for realizing a process of receiving a usage instruction for enabling an information processing device such as the game player model utilization providing device 700 to use a game player model that has learned the behavior of the learning subject in a second playing state based on play data including a first playing state in a game and an action taken by the player in the first playing state and a label indicating whether or not the learning subject is a learning subject, and outputting model information for using the game player model in response to the usage instruction.

[0099] Furthermore, the game player model utilization providing method executed by an information processing device such as the above-mentioned game player model utilization providing device 700 is a method in which the information processing device such as the game player model utilization providing device 700 receives a utilization instruction for making available a game player model that has learned the behavior of the learning subject in a second playing state based on play data including a first playing state in a game and an action taken by the player in the first playing state, and a label indicating whether or not the learning subject is present, and outputs model information for utilizing the game player model in response to the utilization instruction.

[0100] Even if the invention is an invention of a program having the above-mentioned configuration, or a computer-readable recording medium having a program recorded thereon, or a game player model utilization providing method, it can achieve the above-mentioned object of the present invention because it has the same action and effect as the above-mentioned game player model utilization providing device 700.

[0101] <Additional Notes> A part or all of the above-described embodiments may be described as follows: The game play operation learning device and the game player model utilization providing device of the present invention are described below. However, the present invention is not limited to the following configuration.

[0102] (Appendix 1) an acquisition means for acquiring play data including a first play state in a game and an action taken by a player in the first play state, and a label indicating whether or not the play state is a learning target; a learning means for generating a game player model for outputting an action of the learning target in response to an input of a second play state based on the play data and the label; an output means for outputting the game player model; A game play operation learning device comprising: (Appendix 2) The label is either a first label that is assigned to play data having an attribute to be learned, or a second label that is different from the first label and that is assigned to play data having an attribute different from the attribute to be learned, The learning means performs machine learning using the play data to which the first label has been assigned and the play data to which the second label has been assigned. 2. A game play operation learning device as described in appendix 1. (Appendix 3) the label is either a first label that is assigned to play data having an attribute to be learned, or a second label that is different from the first label and that is assigned to play data having an attribute that is opposite to the attribute to be learned; The learning means performs machine learning using the play data to which the first label has been assigned and the play data to which the second label has been assigned. 3. A game play operation learning device according to claim 1 or 2. (Appendix 4) The learning means performs machine learning so as to approach the play data to which the first label is assigned and to move away from the play data to which the second label is assigned. 4. A game play operation learning device according to claim 2 or 3. (Appendix 5) The second label is assigned to play data having an attribute indicating that the player is an artificial intelligence; The learning means performs machine learning using the play data to which the first label has been assigned and the play data to which the second label has been assigned and which has an attribute indicating that the player is an artificial intelligence. 5. A game play operation learning device according to any one of claims 2 to 4. (Appendix 6) The first label is assigned to play data having an attribute indicating that the player is a specific person. 6. A game play operation learning device according to any one of claims 2 to 5. (Appendix 7) a voice information acquiring means for acquiring voice information indicating a voice of a player, The output means outputs the audio information. 7. A game play operation learning device according to any one of claims 1 to 6. (Appendix 8) The play data includes the first play state, the action taken by the player in the first play state, and a third play state resulting from the action. 8. A game play operation learning device according to any one of claims 1 to 7. (Appendix 9) An information processing device, acquiring play data including a first play state in a game and an action taken by a player in the first play state, and a label indicating whether or not the play state is a learning target; A game player model is generated based on the play data and the label to output the behavior of the learning target in response to the input of a second play state. How to learn gameplay controls. (Appendix 10) In the information processing device, acquiring play data including a first play state in a game and an action taken by a player in the first play state, and a label indicating whether or not the play state is a learning target; A game player model is generated based on the play data and the label to output the behavior of the learning target in response to the input of a second play state. A computer-readable recording medium that records a program for implementing processing. (Appendix 11) a receiving means for receiving a usage instruction for enabling the use of a game player model that has learned the behavior of the learning subject in a second play state based on play data including a first play state in a game and an action taken by a player in the first play state and a label indicating whether or not the learning subject is a learning subject; an output means for outputting model information for using the game player model in response to the usage instruction; A game player model utilization providing device comprising: (Appendix 12) A billing means for requesting a model usage fee from a device that uses the game player model. 12. The game player model utilization providing device according to claim 11. (Appendix 13) an instruction means for instructing a learning device to create the game player model in response to an instruction to create the game player model; The billing means, when receiving the instruction to create the game player model, requests a registration fee from the external device that is the source of the instruction to create the game player model. 13. A game player model utilization providing device as described in appendix 12. (Appendix 14) The billing means requests a registration fee when the external device that is the source of the creation instruction satisfies a predetermined condition. 14. A game player model utilization providing device as described in appendix 13. (Appendix 15) The output means further provides voice information indicative of a voice of a player. A game player model utilization providing device according to any one of appendix 11 to appendix 14. (Appendix 16) The output means provides the game player model created in a state in which a first label is assigned to play data having an attribute to be learned, and a second label different from the first label is assigned to play data having an attribute opposite to the attribute to be learned. A game player model utilization providing device according to any one of appendix 11 to appendix 15. (Appendix 17) The game player model is a model generated in a state in which a first label is assigned to play data having an attribute indicating that the player is a specific person, and a second label is assigned to play data having an attribute indicating that the player is an artificial intelligence. A game player model utilization providing device according to any one of appendix 11 to appendix 16. (Appendix 18) The game player model is a model generated by machine learning so as to approach play data to which a first label has been assigned and to move away from play data to which a second label has been assigned. A game player model utilization providing device according to any one of appendix 11 to appendix 17. (Appendix 19) An information processing device, receiving a usage instruction for enabling use of a game player model that has learned the behavior of the learning subject in a second play state based on play data including a first play state in a game and an action taken by a player in the first play state and a label indicating whether the learning subject is a learning subject; In response to the usage instruction, model information for using the game player model is output. Game player model usage provision method. (Appendix 20) In the information processing device, receiving a usage instruction for enabling use of a game player model that has learned the behavior of the learning subject in a second play state based on play data including a first play state in a game and an action taken by a player in the first play state and a label indicating whether the learning subject is a learning subject; In response to the usage instruction, model information for using the game player model is output. A computer-readable recording medium that records a program for implementing processing.

[0103] Although the present invention has been described with reference to the above-mentioned embodiments, the present invention is not limited to the above-mentioned embodiments. Various modifications that can be understood by a person skilled in the art can be made to the configuration and details of the present invention within the scope of the present invention. [Explanation of symbols]

[0104] 100 Learning Device 110 Communication I / F section 120 Storage section 121 Input Data 122 Neural Networks 123 Programs 124 Audio Information 130 Processing section 131 Acquisition Department 132 Learning Department 133 Output section 134 Voice information acquisition unit 200 Learning System 300 Customer terminals 310 Play Data Acquisition Department 320 Transmitter 330 Usage Instructions Department 400 Server device 410 Communication I / F section 420 Storage section 421 Play Data Information 422 Created Model Information 423 Program 430 Processing Unit 431 Play data receiver 432 Creation Instruction Transmitting and Receiving Unit 433 Created Model Reception Unit 434 User Instructions Reception Department 435 Output section 436 Billing Department 500 Learning Device 600 Game Play Operation Learning Device 601 CPU 602 ROM 603 RAM 604 Programs 605 Storage device 606 Drive Unit 607 Communication Interface 608 Input / Output Interface 609 Bus 610 Recording media 611 Communication Network 621 Acquisition means 622 Learning Methods 623 Output Means 700 Game player model utilization providing device 721 Reception method 722 Output Method

Claims

1. a receiving means for receiving a usage instruction for enabling the use of a game player model that has learned the behavior of the learning subject in a second play state based on play data including a first play state in a game and an action taken by a player in the first play state and a label indicating whether or not the learning subject is a learning subject; an output means for outputting model information for using the game player model in response to the usage instruction; Equipped with the game player model is a model generated in a state in which a first label is assigned to gameplay data having an attribute indicating that the player is a specific person, and a second label is assigned to gameplay data having an attribute indicating that the player is an artificial intelligence, The game player model is a model generated by machine learning so as to approach play data to which a first label has been assigned and to move away from play data to which a second label has been assigned. Game player model utilization providing device.

2. A billing means for requesting a model usage fee from a device that uses the game player model.

2. The game player model utilization and provision device according to claim 1.

3. an instruction means for instructing a learning device to create the game player model in response to an instruction to create the game player model; The billing means, when receiving an instruction to create the game player model, requests a registration fee from the external device that is the source of the instruction to create the game player model.

3. The game player model utilization and provision device according to claim 2.

4. The billing means requests a registration fee when the external device that is the source of the creation instruction satisfies a predetermined condition.

4. The game player model utilization and provision device according to claim 3.

5. The output means further provides voice information indicative of a voice of a player.

2. The game player model utilization and provision device according to claim 1.

6. An information processing device, receiving a usage instruction for enabling use of a game player model that has learned the behavior of the learning subject in a second play state based on play data including a first play state in a game and an action taken by a player in the first play state and a label indicating whether the learning subject is a learning subject; outputting model information for using the game player model in response to the usage instruction; the game player model is a model generated in a state in which a first label is assigned to gameplay data having an attribute indicating that the player is a specific person, and a second label is assigned to gameplay data having an attribute indicating that the player is an artificial intelligence, The game player model is a model generated by machine learning so as to approach play data to which a first label has been assigned and to move away from play data to which a second label has been assigned. Game player model usage provision method.

7. In the information processing device, receiving a usage instruction for enabling use of a game player model that has learned the behavior of the learning subject in a second play state based on play data including a first play state in a game and an action taken by a player in the first play state and a label indicating whether the learning subject is a learning subject; In response to the usage instruction, a process of outputting model information for using the game player model is realized; the game player model is a model generated in a state in which a first label is assigned to gameplay data having an attribute indicating that the player is a specific person, and a second label is assigned to gameplay data having an attribute indicating that the player is an artificial intelligence, The game player model is a model generated by machine learning so as to approach play data to which a first label has been assigned and to move away from play data to which a second label has been assigned. program.

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