CN108920213B - Dynamic configuration method and device of game - Google Patents

Dynamic configuration method and device of game Download PDF

Info

Publication number
CN108920213B
CN108920213B CN201810712906.4A CN201810712906A CN108920213B CN 108920213 B CN108920213 B CN 108920213B CN 201810712906 A CN201810712906 A CN 201810712906A CN 108920213 B CN108920213 B CN 108920213B
Authority
CN
China
Prior art keywords
player
game
data
game setting
setting data
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN201810712906.4A
Other languages
Chinese (zh)
Other versions
CN108920213A (en
Inventor
佟卉斌
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Super Magic Cube Beijing Technology Co ltd
Original Assignee
Super Magic Cube Beijing Technology Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Super Magic Cube Beijing Technology Co Ltd filed Critical Super Magic Cube Beijing Technology Co Ltd
Priority to CN201810712906.4A priority Critical patent/CN108920213B/en
Publication of CN108920213A publication Critical patent/CN108920213A/en
Application granted granted Critical
Publication of CN108920213B publication Critical patent/CN108920213B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/44—Arrangements for executing specific programs
    • G06F9/445—Program loading or initiating
    • G06F9/44505—Configuring for program initiating, e.g. using registry, configuration files
    • G06F9/4451—User profiles; Roaming
    • A—HUMAN NECESSITIES
    • A63—SPORTS; GAMES; AMUSEMENTS
    • A63F—CARD, BOARD, OR ROULETTE GAMES; INDOOR GAMES USING SMALL MOVING PLAYING BODIES; VIDEO GAMES; GAMES NOT OTHERWISE PROVIDED FOR
    • A63F13/00—Video games, i.e. games using an electronically generated display having two or more dimensions
    • A63F13/50—Controlling the output signals based on the game progress
    • A63F13/52—Controlling the output signals based on the game progress involving aspects of the displayed game scene
    • A—HUMAN NECESSITIES
    • A63—SPORTS; GAMES; AMUSEMENTS
    • A63F—CARD, BOARD, OR ROULETTE GAMES; INDOOR GAMES USING SMALL MOVING PLAYING BODIES; VIDEO GAMES; GAMES NOT OTHERWISE PROVIDED FOR
    • A63F2300/00—Features of games using an electronically generated display having two or more dimensions, e.g. on a television screen, showing representations related to the game
    • A63F2300/30—Features of games using an electronically generated display having two or more dimensions, e.g. on a television screen, showing representations related to the game characterized by output arrangements for receiving control signals generated by the game device
    • A—HUMAN NECESSITIES
    • A63—SPORTS; GAMES; AMUSEMENTS
    • A63F—CARD, BOARD, OR ROULETTE GAMES; INDOOR GAMES USING SMALL MOVING PLAYING BODIES; VIDEO GAMES; GAMES NOT OTHERWISE PROVIDED FOR
    • A63F2300/00—Features of games using an electronically generated display having two or more dimensions, e.g. on a television screen, showing representations related to the game
    • A63F2300/30—Features of games using an electronically generated display having two or more dimensions, e.g. on a television screen, showing representations related to the game characterized by output arrangements for receiving control signals generated by the game device
    • A63F2300/308—Details of the user interface

Abstract

The invention provides a dynamic configuration method of a game, which comprises the following steps: acquiring historical behavior information of a player in a game; obtaining a player portrait corresponding to the player according to the historical behavior information; determining target game setting data of the player based on the player representation; and after the game is started, configuring a corresponding game environment for the player according to the target game setting data. By the configuration method, the game environment can be dynamically adjusted according to the characteristics of each player, so that the game environment is more in line with the game preference and the game intention of the player, the game setting and the preference of the player are in good interaction, and better game experience is provided for the player.

Description

Dynamic configuration method and device of game
Technical Field
The invention relates to the field of online games, in particular to a dynamic configuration method and a dynamic configuration device for games.
Background
In a large network game, there are typically many elements such as values, maps, scenes, skill classifications and ratings, combat and defeat rules, which may be collectively referred to as environmental factors describing the environmental conditions and constraints faced by the players throughout the game. The player interacts with other players during the course of the game based on these elements. Whether the design of the elements is reasonable and interesting or not can bring fun to the player is the key of whether the game can retain the player or not, namely the key of game success or failure.
In current game production, these elements are set and adjusted by the game planner based on his professional experience prior to game release. And network games often have only one set of configuration systems, i.e., the environmental factors are often the same for all players. After the game is released, although the environmental factors can be corrected according to the experience and the value of the player and through research and feedback on the player, the aim of maintaining good operation of the game is achieved, the correction can only be unified correction on the environmental factors, and is consistent for all users.
The unified setting and modifying mode can not meet the individual requirements of thousands of players and can not adjust the game environment factors in time.
Disclosure of Invention
The present invention is directed to solve at least one of the above technical problems in the related art to a certain extent, and to provide a method and an apparatus for dynamically configuring a game.
In order to achieve the above object, an embodiment according to a first aspect of the present invention proposes a dynamic configuration method of a game, including: acquiring historical behavior information of a player in a game; obtaining a player portrait corresponding to the player according to the historical behavior information; determining target game setting data of the player based on the player representation; and after the game is started, configuring a corresponding game environment for the player according to the target game setting data.
In the embodiment of the invention, the player portrait corresponding to the player can be obtained according to the historical behavior information of the player, and the corresponding game environment is loaded according to the player portrait in the process of game playing, thereby meeting the personalized requirements of the player. By the configuration method of the embodiment of the invention, the game environment can be dynamically adjusted according to the characteristics of each player, so that the game environment is more in line with the game preference and the game intention of the player, the game setting and the preference of the player are enabled to carry out benign interaction, and better game experience is provided for the player.
In some embodiments of the invention, said generating a player representation corresponding to the player based on the historical behavior information comprises: extracting player behavior data and game setting data corresponding to the player behavior data from the historical behavior information; and generating a player portrait corresponding to the player by using a player classification model according to the player behavior data and the game setting data, wherein the player classification model is obtained by self-learning based on a deep neural network model.
In the embodiment of the invention, the player classification model is generated through the deep neural network model, so that the player classification model is more accurate, and the judgment precision is improved.
In some embodiments of the invention, said generating a player representation corresponding to the player using a player classification model based on the player behavior data and the game setting data comprises: querying the player classification model according to the player behavior data and the game setting data to obtain a plurality of player characteristics corresponding to the player behavior data and the game setting data; and generating a player representation corresponding to the player based on the plurality of player characteristics.
In the embodiment of the invention, the player behavior data and the game setting data can reflect the preference of the player, and a plurality of player characteristics corresponding to the player can be determined according to the player behavior data and the game setting data.
In some embodiments of the present invention, the determining target game setting data corresponding to the player based on the player representation includes: determining a type to which the player representation belongs based on the player representation; obtaining an adjustment strategy of game setting corresponding to the type according to the type of the player portrait; and determining target game setting data corresponding to the player according to the adjustment strategy.
In the embodiment of the present invention, the type of the player can be determined from the player figure, and the target game setting data preferred by the player can be generated according to the type of the player, so that more accurate target game setting data can be obtained.
In some embodiments of the invention, further comprising: obtaining historical behavior sample data of a plurality of sample players in a game and game setting sample data corresponding to the historical behavior sample data; and training the player classification model according to the historical behavior sample data and the game setting sample data of the plurality of sample players in the game.
In the embodiment of the invention, the player classification model is trained through the historical data of a plurality of sample players, so that the accuracy of the player classification model can be improved.
In order to achieve the above object, an embodiment according to a second aspect of the present invention proposes a dynamic configuration apparatus of a game, including: the behavior data acquisition module is used for acquiring historical behavior information of the player in the game; a player representation generation module for acquiring a player representation corresponding to the player according to the historical behavior information; a target game setting data acquisition module for determining target game setting data of the player according to the player figure; and the game configuration module is used for configuring a corresponding game environment for the player according to the target game setting data after the game is started. In the embodiment of the invention, the player portrait corresponding to the player can be obtained according to the historical behavior information of the player, and the corresponding game environment is loaded according to the player portrait in the process of game playing, thereby meeting the personalized requirements of the player. By the configuration method of the embodiment of the invention, the game environment can be dynamically adjusted according to the characteristics of each player, so that the game environment is more in line with the game preference and the game intention of the player, the game setting and the preference of the player are enabled to carry out benign interaction, and better game experience is provided for the player.
In some embodiments of the invention, the player representation generation module extracts player behavior data and game setting data corresponding to the player behavior data from the historical behavior information and generates the player representation corresponding to the player using a player classification model based on the player behavior data and the game setting data, wherein the player classification model is self-learned based on a deep neural network model. In the embodiment of the invention, the player classification model is generated through the deep neural network model, so that the player classification model is more accurate, and the judgment precision is improved.
In some embodiments of the invention, the player representation generation module queries the player classification model based on the player behavior data and the game setting data to obtain a plurality of player characteristics corresponding to the player behavior data and the game setting data, and generates a player representation corresponding to the player based on the plurality of player characteristics. In the embodiment of the invention, the player behavior data and the game setting data can reflect the preference of the player, and a plurality of player characteristics corresponding to the player can be determined according to the player behavior data and the game setting data.
In some embodiments of the present invention, the target game setting data obtaining module determines a type to which the player representation belongs based on the player representation, obtains an adjustment policy for game setting corresponding to the type based on the type to which the player representation belongs, and determines target game setting data corresponding to the player based on the adjustment policy. In the embodiment of the present invention, the type of the player can be determined from the player figure, and the target game setting data preferred by the player can be generated according to the type of the player, so that more accurate target game setting data can be obtained.
In some embodiments of the invention, further comprising: the neural network training module is used for acquiring historical behavior sample data of a plurality of sample players in a game and game setting sample data corresponding to the historical behavior sample data, and training the player classification model according to the historical behavior sample data of the plurality of sample players in the game and the game setting sample data. In the embodiment of the invention, the player classification model is trained through the historical data of a plurality of sample players, so that the accuracy of the player classification model can be improved.
In order to achieve the above object, an embodiment of a third aspect of the present invention provides a non-transitory computer readable storage medium, on which a computer program is stored, which when executed by a processor, implements a method for dynamically configuring a game according to the first aspect of the present invention.
In order to achieve the above object, a fourth aspect of the present invention provides a computer program product, wherein instructions of the computer program product, when executed by a processor, implement the dynamic configuration method of the game according to the first aspect of the present invention.
In order to achieve the above object, an embodiment of a fifth aspect of the present invention provides a computing device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the dynamic configuration method of the game according to the first aspect of the present invention.
The non-transitory computer-readable storage medium, the computer program product and the computing device according to the third to fifth aspects of the present invention have similar advantageous effects to the dynamic configuration method and apparatus of the game according to the first and second aspects of the present invention, and will not be described herein again.
Drawings
The foregoing and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a schematic flow diagram of a method for dynamic game configuration based on artificial intelligence, in accordance with an embodiment of the invention;
FIG. 2 is a schematic diagram of a multi-layered perceptron deep neural network architecture according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of the operation of a dynamic game configuration method for artificial intelligence in accordance with an embodiment of the invention;
FIG. 4 is a block diagram of an artificial intelligence based dynamic game configuration apparatus according to an embodiment of the invention;
FIG. 5 is a schematic structural diagram of a computing device according to an embodiment of the invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative and intended to be illustrative of the invention and are not to be construed as limiting the invention.
Aiming at the problem that in the prior art, when game environment parameters are set and modified, all players take effect and different individual requirements of individual players cannot be met. In view of the above, the present invention provides a method and an apparatus for dynamically configuring a game.
The embodiment of the invention can load different game environments for different players in the game process according to the player figures. Therefore, the game environment can be configured individually aiming at different game requirements of each player, even the same player in different stages, and better game experience is provided for the player.
The method and apparatus of embodiments of the present invention are described in detail below with reference to the accompanying drawings.
Referring to fig. 1, the dynamic configuration method of a game according to the present invention may include steps S110 to S140.
In step S110, historical behavior information of the player in the game is acquired.
Specifically, the historical behavior information may include player behavior data of the player and game setting data corresponding to the player behavior data. That is, the historical operation behavior of the player during playing the game and the setting data generated by the historical operation behavior, such as the setting operation and operation result of the game, the selection operation and operation result of the character, and so on. The player behavior data refers to data of a relevant operation behavior of a player in a game, and the game setting data refers to setting data that changes in accordance with the relevant operation behavior in a game environment. For the purpose of feedback and adjustment of game settings, the game server needs to record the historical behavior information of the player in real time.
The concept of deep learning is derived from artificial neural networks, which generally refers to the training and application of neural network structures or models with a large number of hidden layers. In the embodiment of the invention, in order to realize accurate identification of the intention (task, long-term monster, bonus) of a player, character characteristics (adventure, upgrade, and equipment), various settings (task system setting, monster difficulty, course difficulty) in a game by a deep learning mode, historical behavior information of the player in the game and corresponding game setting data when the historical behavior occurs are generally acquired and input into a neural network model so as to obtain a player image of the player.
In the embodiment of the present invention, for example, specific behavior data having a high degree of correlation with the creation of the player figure and game setting data corresponding to the specific behavior data may be collected. In the embodiment of the present invention, the player behavior data refers to data of various operation behaviors performed by the player in the game, for example, the player behavior data may include a player account, a player login time, a game duration (which may be obtained according to behaviors of the player logging in and out), a number and a type of missions, a fighting behavior, an equipment increase and decrease behavior, a player mission result data, and a fighting result data. The game setting data corresponding to the player behavior data is data set in a game environment corresponding to the player behavior data when the player performs the player behavior, and particularly, data that can be set individually and adaptively according to the player. In the embodiment of the present invention, the data of the game environment may be set by the game server, wherein some of the data may be fixed in the whole game, for example, a map of the game, a background year, attribute parameter level division of a character, and the like are mostly set to be fixed, while some of the game environment data may be personalized according to different players, and in the embodiment of the present invention, the data of the game environment is specifically set for these variable environment data.
Since those fixed environment data are constant for the entire game, they may not be recorded every time. Thus, in some embodiments, the historical behavior information obtained in this step may include only those dynamic game environment data whose settings may be changed. Or, determining data with higher correlation with player behaviors in the game environment data through machine learning and theoretical analysis, and recording the historical behavior information with higher correlation.
For example, the recorded game environment setting data may include setting data of a mission system, setting data of a combat object, difficulty of an alternative track, and the like. The game experience of the player can be improved more remarkably by individually adjusting the game setting data with higher relevance to the game effect and experience of the player, such as the setting data of the mission system, the setting data of the combat target, the difficulty of the alternative track and the like.
Step S120, obtaining the corresponding player portrait of the player according to the historical behavior information.
In embodiments of the present invention, a player classification model may be constructed from a deep neural network-based model by accumulating game behavior data and corresponding game setting data. The player classification model may perform feature extraction on the player's game preferences, character characteristics, current game intent, etc., to portray each player. The player portrait is also called user character, and is an effective tool for delineating target player, player appeal and design direction in the design of the network game. A player representation may be described by a set of player characteristics, which may be the background color of the game, character preferences, and so forth. Specifically, the player image may represent information such as player operation habits, frustration resistance, and game duration.
In some embodiments, the obtaining a representation of the player using a deep neural network model for data processing based on the set of player behavior data and game setting data corresponding to the behavior data may include: using a deep neural network based player classification model; taking behavior data of a player and game setting data corresponding to the behavior data as input of a neural network classification model, and inputting the input into the neural network classification model; the output of the neural network classification model is player characteristics, and the player characteristics embody game preferences of players, such as setting of background, selection of background music and the like; a player representation is generated based on the player characteristics output by the neural network classification model. In embodiments of the present invention, the player characteristics refer to the player's tag, such as lively players and the like. In an embodiment of the invention, the player representation is comprised of a plurality of player characteristics, such as lively type figure players.
Players are classified into different categories according to their characteristics, and the actions they would take for different game settings are predicted for different player categories. After the portraits of a plurality of players are accumulated, the setting of the game can be intelligently adjusted according to the portraits of the players in the game, so that the players can more comfortably obtain the personalized experience in the game.
Referring to fig. 2, fig. 2 is a schematic structural diagram of a multilayer perceptron deep neural network according to an embodiment of the present invention. The operation of the deep neural network is described by taking a multi-layer perceptron (MLP) model as an example, but it should be noted that the player classification model based on the deep neural network is not limited to the implementation in the MLP form, and various classifier models in various existing neural network structures can be applied to the deep neural network.
Wherein, when the player classification model is operated, the player behavior data and the game setting data corresponding to the player behavior are used as the input X of the model1To XnModel output C1To CnFor player characteristics, according to player characteristics C1To CnA player representation Y is determined. For example, player characteristics may include: grading information of the players in the aspects of item purchase, battle, social contact, retention and the like in the game. For example, for each feature, it is described as being divided into several levels to evaluate whether the player's preference for combat is high or low, social activity level is high or low, and time and probability of survival, etc.
Step S130, determining the target game setting data of the player according to the player image.
In some embodiments of the present invention, the target game setting data refers to the image of the player, the preference setting data of the player, such as the favorite background color, background music, character type, etc. of the player, and the favorite game scenario of the user, such as a game more focused on strangeness or social interaction.
The target game setting data corresponding to the player is determined based on the player figure, and may be determined for each category of player based on operation experience, psychology, user research, etc. In this case, target game setting data corresponding to the player can be determined based on the player image according to a preset correspondence between the player image and the target game setting data.
In addition, in other embodiments, the deep neural network model may be used to predict player behavior that may be taken by different players under various game settings, and to design and modify the game settings accordingly. According to the player portrait, the deep neural network model is used for predicting actions which the player tends to take under different game settings, and corresponding game environment settings are carried out in advance.
According to the embodiment of the invention, each player is dynamically tracked, the parameter setting of the game environment is dynamically modified according to the latest image of the player, the game environment which is more in line with the intention and the character characteristics of the player is provided for the player, the environment parameters which enable the player to more probably make a target behavior are set, and the game experience which is more in line with the expectation is provided for the user, so that the retention rate of the user is increased.
The method of determining the target game setting data corresponding to the player according to the preset corresponding relationship between the player figure and the target game setting data is more suitable for the scene with relatively fixed game experience requirements of the user. In this case, it may be considered that the expected user behavior is fixed, and an expected behavior evaluation index function (for example, a function parameter of each specific behavior) may be set as a basis for optimizing the game setting data.
Step S140, after the game is started, configuring a corresponding game environment for the player according to the target game setting data.
When a player logs in a game, the game server can acquire target game setting data corresponding to the player and configure a game environment according to the target game setting data after the game is started. For example, game settings data may be dynamically modified for player characteristics, to provide more missions to players who like social or tasking, to provide more monsters to players who like combat bizarre, or to modify the background color or background music of the game to the player's favorite color or music, within the overall framework of the game.
In some embodiments, the deep neural network model may be trained in advance according to historical behavior data of a large number of players and game setting data corresponding to the behavior data, so as to obtain a player classification model based on the deep neural network. Then, the player model is online to run in real time, the change of the player portrait is tracked in real time, and the player model can be continuously trained and corrected in the running process.
Referring to fig. 3, fig. 3 is a schematic diagram of an operation process of a dynamic game configuration method of artificial intelligence according to an embodiment of the present invention.
First, player behavior data and corresponding game setting data are acquired. The player behavior data and corresponding game setting data are then input into a deep neural network model (i.e., a player classification model of an embodiment of the present invention). The deep neural network model generates a corresponding player figure based on the player behavior data of the player and the corresponding game setting data, and transmits the player figure to the game server. The game server generates target game setting data based on the player image, and allocates a game environment corresponding to the player based on the target game setting data when the game is started. For example, the background color of the game is set to a color preferred by the player, or the scenario of the game is set to a scenario preferred by the player, and the like.
Specifically, when a player logs in to play a game, the game server acquires a player representation of the player from a deep neural network model (i.e., a player classification model). Further, a game environment configuration (i.e., a game environment corresponding to the target game setting data) suitable for the player is generated based on the player image. Meanwhile, during the game of the player, the game server will continue to collect the behavior data and game setting data of the player and continuously update the player portrait, i.e. repeatedly execute the above-mentioned learning and adjusting processes. Therefore, the aim that the game server dynamically adjusts the image of the player according to the characteristics of each player is achieved, and the game setting and the preference of the player are enabled to be in good interaction.
The dynamic configuration method of the game can acquire the player portrait corresponding to the player according to the historical behavior information of the player, and loads the corresponding game environment according to the player portrait in the process of game progress, thereby meeting the individual requirements of the player. The deep neural network model is used for modeling the relationship between the player behavior and the game setting and the player characteristics, and the game setting can be dynamically adjusted according to the characteristics of each player, so that the game setting is more in line with the game preference and the game intention of the player, the game setting and the preference of the player are in good interaction, and better game experience is provided for the player.
To implement the method in the above-described embodiment of the first aspect, an embodiment of the second aspect of the present invention proposes a dynamic configuration apparatus for a game.
Implementations of the systems/apparatus may include one or more computing devices including a processor and a memory having stored thereon an application program including computer program instructions executable on the processor. The application program may be divided into a plurality of program modules for respective functions of the respective components of the system. The modules of the program are logically divided rather than physically, each program module may run on one or more computing devices, and one or more program modules may run on one computing device. The system/apparatus of the present invention is described in further detail below in terms of the functional logical division of program modules.
FIG. 4 is a block diagram of an artificial intelligence based dynamic game configuration device according to an embodiment of the present invention. An apparatus 100 according to the present invention may include a behavior data acquisition module 110, a player representation generation module 120, a target game setting data acquisition module 130, and a game configuration module 140. The behavior data acquiring module 110 is configured to acquire historical behavior information of a player in a game; a player figure generation module 120 for obtaining a player figure corresponding to the player based on the historical behavior information; a target game setting data obtaining module 130 for determining target game setting data of the player based on the player representation; and the game configuration module 140 is configured to configure a corresponding game environment for the player according to the target game setting data after the game is started. In the embodiment of the invention, the player portrait corresponding to the player can be obtained according to the historical behavior information of the player, and the corresponding game environment is loaded according to the player portrait in the process of game playing, thereby meeting the personalized requirements of the player. By the configuration method of the embodiment of the invention, the game environment can be dynamically adjusted according to the characteristics of each player, so that the game environment is more in line with the game preference and the game intention of the player, the game setting and the preference of the player are enabled to carry out benign interaction, and better game experience is provided for the player.
In some embodiments of the invention, the player representation generation module extracts player behavior data and game setting data corresponding to the player behavior data from the historical behavior information and generates the player representation corresponding to the player using a player classification model based on the player behavior data and the game setting data, wherein the player classification model is self-learned based on a deep neural network model. In the embodiment of the invention, the player classification model is generated through the deep neural network model, so that the player classification model is more accurate, and the judgment precision is improved.
In some embodiments of the present invention, the player representation generation module 120 queries the player classification model based on the player behavior data and the game setting data to obtain a plurality of player characteristics corresponding to the player behavior data and the game setting data, and generates a player representation corresponding to the player based on the plurality of player characteristics. In the embodiment of the invention, the player behavior data and the game setting data can reflect the preference of the player, and a plurality of player characteristics corresponding to the player can be determined according to the player behavior data and the game setting data.
In some embodiments of the present invention, the target game setting data obtaining module 130 determines a type of the player representation, obtains an adjustment policy of the game setting corresponding to the type, and determines the target game setting data corresponding to the player according to the adjustment policy. In the embodiment of the present invention, the type of the player can be determined from the player figure, and the target game setting data preferred by the player can be generated according to the type of the player, so that more accurate target game setting data can be obtained.
In some embodiments of the invention, further comprising: the neural network training module is used for acquiring historical behavior sample data of a plurality of sample players in a game and game setting sample data corresponding to the historical behavior sample data, and training the player classification model according to the historical behavior sample data of the plurality of sample players in the game and the game setting sample data. In the embodiment of the invention, the player classification model is trained through the historical data of a plurality of sample players, so that the accuracy of the player classification model can be improved.
Embodiments of the third aspect of the present invention provide a non-transitory computer readable storage medium having stored thereon executable instructions which, when run on a processor, implement a method of dynamically configuring a game as described in embodiments of the first aspect of the present invention. The storage medium may be provided thereon as part of the apparatus; or the storage medium may be provided on a remote server that controls the device, when the device may be remotely controlled by the server.
Computer instructions for carrying out the methods of the present invention may be carried using any combination of one or more computer-readable media. By non-transitory computer readable medium can be included any computer readable medium except for the signal itself, which is temporarily propagating. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
An embodiment of a fourth aspect of the present invention provides a computer program product, wherein instructions of the computer program product, when executed by a processor, implement a method for dynamically configuring a game according to an embodiment of the first aspect of the present invention.
Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
In an embodiment of the fifth aspect of the present invention, there is provided a computing device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor executes the computer program to implement the method for dynamically configuring a game according to the first aspect of the present invention.
The foregoing detailed description of the storage medium, the computer program product and the computing device according to the third to fifth aspects of the present invention can be obtained from the corresponding embodiments of the method and the apparatus for configuring a dynamic game based on artificial intelligence of the present invention, and have similar beneficial effects to the corresponding method and the apparatus for configuring a dynamic game based on artificial intelligence of the present invention, and will not be described herein again.
FIG. 5 illustrates a block diagram of an exemplary computing device suitable for use in implementing embodiments of the present disclosure. The computing device 12 shown in FIG. 5 is only one example and should not impose any limitations on the functionality or scope of use of embodiments of the disclosure.
As shown in FIG. 5, computing device 12 may be implemented in the form of a general purpose computing device. Components of computing device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that couples various system components including the system memory 28 and the processing unit 16.
Bus 18 represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. These architectures include, but are not limited to, Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MAC) bus, enhanced ISA bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus, to name a few.
Computing device 12 typically includes a variety of computer system readable media. Such media may be any available media that is accessible by computing device 12 and includes both volatile and nonvolatile media, removable and non-removable media.
Memory 28 may include computer system readable media in the form of volatile Memory, such as Random Access Memory (RAM) 30 and/or cache Memory 32. Computing device 12 may further include other removable/non-removable, volatile/nonvolatile computer-readable storage media. By way of example only, storage system 34 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown, but commonly referred to as a "hard drive"). Although not shown in FIG. 5, a disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a Compact disk Read Only Memory (CD-ROM), a Digital versatile disk Read Only Memory (DVD-ROM), or other optical media) may be provided. In these cases, each drive may be connected to bus 18 by one or more data media interfaces. Memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the disclosure.
A program/utility 40 having a set (at least one) of program modules 42 may be stored, for example, in memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination thereof may comprise an implementation of a network environment. Program modules 42 generally perform the functions and/or methodologies of the embodiments described in this disclosure.
Computing device 12 may also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), with one or more devices that enable a user to interact with the computer system/server 12, and/or with any devices (e.g., network card, modem, etc.) that enable the computer system/server 12 to communicate with one or more other computing devices. Such communication may be through an input/output (I/O) interface 22. Moreover, computing device 12 may also communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public Network such as the Internet via Network adapter 20. As shown, network adapter 20 communicates with the other modules of computing device 12 via bus 18. It is noted that although not shown, other hardware and/or software modules may be used in conjunction with computing device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
The processing unit 16 executes various functional applications and data processing, for example, implementing the methods mentioned in the foregoing embodiments, by executing programs stored in the system memory 28.
The non-transitory computer-readable storage medium, the computer program product and the computing device according to the third to fifth aspects of the present invention may be implemented with reference to the contents specifically described in the embodiments according to the first aspect of the present invention, and have similar beneficial effects to the artificial intelligence based dynamic game configuration method according to the first aspect of the present invention, and will not be described herein again.
It should be noted that in the description of the present specification, reference to the description of the term "one embodiment", "some embodiments", "an example", "a specific example", or "some examples", etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "a plurality" means two or more, for example, two, three, etc., unless specifically defined otherwise.
It will be understood by those skilled in the art that all or part of the steps carried by the method implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and the program, when executed, includes one or a combination of the steps of the method embodiments.
In the description of the present specification, any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and alternate implementations are included within the scope of the preferred embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the embodiments of the present invention.
The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.

Claims (10)

1. A method for dynamically configuring a game, comprising:
acquiring historical behavior information of a player in a game;
obtaining a player portrait corresponding to the player according to the historical behavior information;
determining target game setting data of the player based on the player representation; and
after the game is started, configuring a corresponding game environment for the player according to the target game setting data;
the generating a player representation corresponding to the player based on the historical behavior information includes:
extracting player behavior data and game setting data corresponding to the player behavior data from the historical behavior information;
and generating a player portrait corresponding to the player by using a player classification model according to the player behavior data and the game setting data, wherein the player classification model is obtained by self-learning based on a deep neural network model.
2. A method for dynamically configuring a game according to claim 1, wherein said generating a player representation corresponding to said player using a player classification model based on said player behavior data and said game setting data comprises:
querying the player classification model according to the player behavior data and the game setting data to obtain a plurality of player characteristics corresponding to the player behavior data and the game setting data; and
a player representation corresponding to the player is generated based on the plurality of player characteristics.
3. The method of claim 1, wherein determining the target game setting data corresponding to the player according to the player image comprises:
determining a type to which the player representation belongs based on the player representation;
obtaining an adjustment strategy of game setting corresponding to the type according to the type of the player portrait;
and determining target game setting data corresponding to the player according to the adjustment strategy.
4. A method for dynamically configuring a game according to any one of claims 1 to 3, further comprising:
obtaining historical behavior sample data of a plurality of sample players in a game and game setting sample data corresponding to the historical behavior sample data;
and training the player classification model according to the historical behavior sample data and the game setting sample data of the plurality of sample players in the game.
5. A dynamic configuration apparatus for a game, comprising:
the behavior data acquisition module is used for acquiring historical behavior information of the player in the game;
a player representation generation module for acquiring a player representation corresponding to the player according to the historical behavior information, wherein the player representation generation module extracts player behavior data and game setting data corresponding to the player behavior data from the historical behavior information and generates the player representation corresponding to the player by using a player classification model according to the player behavior data and the game setting data, wherein the player classification model is obtained by self-learning based on a deep neural network model;
a target game setting data acquisition module for determining target game setting data of the player according to the player figure;
and the game configuration module is used for configuring a corresponding game environment for the player according to the target game setting data after the game is started.
6. A device for dynamically configuring a game according to claim 5, wherein the player representation generation module queries the player classification model based on the player behavior data and the game settings data to obtain a plurality of player characteristics corresponding to the player behavior data and the game settings data, and generates a player representation corresponding to the player based on the plurality of player characteristics.
7. A game configuration apparatus according to claim 5, wherein said object game setting data obtaining module determines a type to which said player representation belongs based on said player representation, obtains an adjustment policy for a game setting corresponding to said type based on said type to which said player representation belongs, and determines object game setting data corresponding to said player based on said adjustment policy.
8. A dynamic configuration device for a game according to any of claims 5 to 7, further comprising:
the neural network training module is used for acquiring historical behavior sample data of a plurality of sample players in a game and game setting sample data corresponding to the historical behavior sample data, and training the player classification model according to the historical behavior sample data of the plurality of sample players in the game and the game setting sample data.
9. A non-transitory computer readable storage medium having stored thereon a computer program, characterized in that the program, when executed by a processor, implements a method of dynamic configuration of a game according to any of claims 1-4.
10. A computing device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, when executing the program, implementing a method of dynamic configuration of a game according to any of claims 1-4.
CN201810712906.4A 2018-06-29 2018-06-29 Dynamic configuration method and device of game Active CN108920213B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201810712906.4A CN108920213B (en) 2018-06-29 2018-06-29 Dynamic configuration method and device of game

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201810712906.4A CN108920213B (en) 2018-06-29 2018-06-29 Dynamic configuration method and device of game

Publications (2)

Publication Number Publication Date
CN108920213A CN108920213A (en) 2018-11-30
CN108920213B true CN108920213B (en) 2021-09-17

Family

ID=64424574

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201810712906.4A Active CN108920213B (en) 2018-06-29 2018-06-29 Dynamic configuration method and device of game

Country Status (1)

Country Link
CN (1) CN108920213B (en)

Families Citing this family (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109858956B (en) * 2019-01-14 2023-12-12 珠海金山数字网络科技有限公司 Game item pushing method and system based on big data
CN109876454A (en) * 2019-01-14 2019-06-14 珠海金山网络游戏科技有限公司 A kind of game dynamic correcting method and system
CN109806584A (en) * 2019-01-24 2019-05-28 网易（杭州）网络有限公司 Scene of game generation method and device, electronic equipment, storage medium
CN110879730B (en) * 2019-11-14 2023-10-24 腾讯科技（深圳）有限公司 Method and device for automatically adjusting game configuration, electronic equipment and storage medium
CN111714881B (en) * 2020-06-15 2023-08-15 北京智明星通科技股份有限公司 Method, system and terminal for quickly accessing game task based on system message
CN111957053A (en) * 2020-09-03 2020-11-20 网易（杭州）网络有限公司 Game player matching method and device, storage medium and electronic equipment
CN113413607A (en) * 2021-06-28 2021-09-21 网易（杭州）网络有限公司 Information recommendation method and device, computer equipment and storage medium
CN113457166A (en) * 2021-07-20 2021-10-01 网易（杭州）网络有限公司 Game player churn information processing method, device, equipment and storage medium
CN114832385A (en) * 2022-03-29 2022-08-02 山东赤子城网络技术有限公司 Game dynamic adjustment method and system
CN117180730B (en) * 2023-09-08 2024-03-19 广州火石传娱科技有限公司 Toy gun system processing method and system applied to image positioning

Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103412930A (en) * 2013-08-17 2013-11-27 北京品友互动信息技术有限公司 Method for identifying attributes of internet users
CN103826711A (en) * 2011-07-22 2014-05-28 格里奇索弗特公司 Game enhancement system for gaming environment
CN104899040A (en) * 2015-06-12 2015-09-09 安徽游艺道网络科技有限公司 Game background program generation system and method
US9616329B2 (en) * 2012-06-28 2017-04-11 Electronic Arts Inc. Adaptive learning system for video game enhancement
CN107391603A (en) * 2017-06-30 2017-11-24 北京奇虎科技有限公司 User's portrait method for building up and device for mobile terminal
CN107423442A (en) * 2017-08-07 2017-12-01 火烈鸟网络(广州)股份有限公司 Method and system, storage medium and computer equipment are recommended in application based on user's portrait behavioural analysis
CN107885417A (en) * 2017-11-03 2018-04-06 腾讯科技（深圳）有限公司 Object localization method, device and computer-readable recording medium in virtual environment
CN107998661A (en) * 2017-12-26 2018-05-08 苏州大学 A kind of aid decision-making method, device and the storage medium of online battle game
CN108066987A (en) * 2016-11-18 2018-05-25 腾讯科技（深圳）有限公司 Distribution method, device and the server of application scenarios outpost of the tax office combination

Patent Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103826711A (en) * 2011-07-22 2014-05-28 格里奇索弗特公司 Game enhancement system for gaming environment
US9616329B2 (en) * 2012-06-28 2017-04-11 Electronic Arts Inc. Adaptive learning system for video game enhancement
CN103412930A (en) * 2013-08-17 2013-11-27 北京品友互动信息技术有限公司 Method for identifying attributes of internet users
CN104899040A (en) * 2015-06-12 2015-09-09 安徽游艺道网络科技有限公司 Game background program generation system and method
CN108066987A (en) * 2016-11-18 2018-05-25 腾讯科技（深圳）有限公司 Distribution method, device and the server of application scenarios outpost of the tax office combination
CN107391603A (en) * 2017-06-30 2017-11-24 北京奇虎科技有限公司 User's portrait method for building up and device for mobile terminal
CN107423442A (en) * 2017-08-07 2017-12-01 火烈鸟网络(广州)股份有限公司 Method and system, storage medium and computer equipment are recommended in application based on user's portrait behavioural analysis
CN107885417A (en) * 2017-11-03 2018-04-06 腾讯科技（深圳）有限公司 Object localization method, device and computer-readable recording medium in virtual environment
CN107998661A (en) * 2017-12-26 2018-05-08 苏州大学 A kind of aid decision-making method, device and the storage medium of online battle game

Also Published As

Publication number Publication date
CN108920213A (en) 2018-11-30

Similar Documents

Publication Publication Date Title
CN108920213B (en) Dynamic configuration method and device of game
KR102360420B1 (en) Customized models for imitating player gameplay in a video game
US11369880B2 (en) Dynamic difficulty adjustment
CN108499108B (en) Real-time dynamic modification and optimization of game play parameters within a video game application
KR102258278B1 (en) Seasonal reward distribution system
US11712621B2 (en) Video clip classification using feature vectors of a trained image classifier
CN111701246A (en) Game AI decision configuration method and device
JP2022525880A (en) Server load prediction and advanced performance measurement
DE102022114647A1 (en) AUTOMATICALLY GENERATED ADVANCED ACTIVITY AND EVENT SUMMARY FOR GAME SESSIONS
CA3087629C (en) System for managing user experience and method therefor
US20180361251A1 (en) Monitoring game activity to detect a surrogate computer program
Mikkelsen et al. Ethical considerations for player modeling
US11794119B2 (en) Intelligent prioritization and manipulation of stream views
CN111760276B (en) Game behavior control method, device, terminal, server and storage medium
KR20200017326A (en) Apparatus and method for relaxation of game addiction
CN112149798B (en) AI model training method, AI model calling method, apparatus and readable storage medium
US11504618B2 (en) Server-based video help in a video game
CN113710336B (en) Server load prediction and advanced performance metrics
WO2023173833A1 (en) Virtual scene parameter processing methods and apparatuses, electronic device, computer readable storage medium, and computer program product
De Vries et al. Deep learning for classifying Battlefield 4 players
KR20230132943A (en) Method and apparatus for implementing behavior of avatar based on behavior log
CN113791724A (en) Course editing method and device, computer equipment and computer readable storage medium
KR20230115036A (en) Interaction based collaboration agent creation method, collaboration agent provision method, and agent management server for performing the methods
CN111589158A (en) AI model training method, AI model calling method, AI model training device and readable storage medium
JP2021007704A (en) System, method, and program for providing game

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
TA01 Transfer of patent application right

Effective date of registration: 20200921

Address after: No.2202, 1st floor, building 2, No.7 Shixing street, Shijingshan District, Beijing 100041 (cluster registration)

Applicant after: Super magic cube (Beijing) Technology Co.,Ltd.

Address before: 100085 Beijing City, Haidian District Road 33, two floor East Xiaoying

Applicant before: BEIJING KINGSOFT INTERNET SECURITY SOFTWARE Co.,Ltd.

TA01 Transfer of patent application right
GR01 Patent grant
GR01 Patent grant