CN114546116A

CN114546116A - Man-machine chess playing method and device, electronic equipment and storage medium

Info

Publication number: CN114546116A
Application number: CN202210156267.4A
Authority: CN
Inventors: 李文哲; 李凯; 蒲雪; 王兴; 韩殿飞; 蔺颖
Original assignee: Beijing Sensetime Technology Development Co Ltd
Current assignee: Beijing Sensetime Technology Development Co Ltd
Priority date: 2022-02-21
Filing date: 2022-02-21
Publication date: 2022-05-27

Abstract

The present disclosure relates to a man-machine chess-playing method and apparatus, an electronic device, and a storage medium, the method comprising: obtaining first drop point data of current chess step drops of players playing in the current chess game; acquiring second falling point data of the current chess step falling piece recommended and generated by the intelligent chess player according to the chessboard data of the chess player before the current chess step falling piece; and adjusting the chess force level of the intelligent chess according to the first piece point data and the second piece point data, wherein the chess force level determines the third piece point data of the intelligent chess for the next step. The embodiment of the disclosure is beneficial to realizing that the intelligent chess players and the playing players play chess in a balanced and equal way through the flag drums.

Description

Man-machine chess playing method and device, electronic equipment and storage medium

Technical Field

The present disclosure relates to the field of computer technologies, and in particular, to a human-computer playing method and apparatus, an electronic device, and a storage medium.

Background

With the rapid development of the internet, Artificial Intelligence (AI) and intelligent terminal technologies, online chess playing by people and robots, online man-machine interaction chess playing and other modes realized by intelligent terminals have been widely applied to many scenes such as entertainment, teaching, competition and the like.

In man-machine chess playing, in order to enable the users who participate in chess playing to play the chess continuously, how to enable the artificial intelligence engine to become a better partner-training player is important to play chess which is equivalent to the flag drums of the users who participate in chess playing.

Disclosure of Invention

The present disclosure provides a technical scheme of man-machine chess playing.

According to an aspect of the present disclosure, there is provided a man-machine playing method including: obtaining first drop point data of current chess step drops of players playing in the current chess game; acquiring second drop point data of the current chess step drop recommended by the intelligent chess player according to the chessboard data of the chess player before the current chess step drop; and adjusting the chess force level of the intelligent chess player according to the first piece point data and the second piece point data, wherein the chess force level determines the third piece point data of the intelligent chess player for the next step.

In one possible implementation, the method further includes: before the current chess game is played, adjusting the initial chess force level of the intelligent chess player in the current chess game according to the chess game information of the last chess game of the playing player, wherein the chess game information comprises the playing process of the playing player recorded by the intelligent chess player in the interactive playing process.

In one possible implementation, the adjusting the playing force level of the intelligent player according to the first and second fall-point data includes: determining a difference between the moving method of the first falling point data and the moving method of the second falling point data according to the first falling point data and the second falling point data, wherein the difference represents a difference of levels of good and bad chess force levels between the moving methods; and determining the chess force level of the intelligent chess player corresponding to the next chess step according to the difference and the initial chess force level.

In one possible implementation, the adjusting the playing force level of the smart player according to the first and second fall-point data includes: determining a difference between the moving method of the first falling point data and the moving method of the second falling point data according to the first falling point data and the second falling point data, wherein the difference represents a difference of levels of good and bad chess force levels between the moving methods; and determining the chess force level of the intelligent chess hand corresponding to the step of the next chess according to the difference and the chess force level of the intelligent chess hand corresponding to the step of the previous chess.

In a possible implementation manner, the obtaining second point data of the current step drop recommended by the intelligent player according to the board data before the current step drop of the playing player includes: according to the chessboard data of the players before the current chess step is dropped, the intelligent chess players generate M recommended walking methods corresponding to the current chess step; and sorting the M recommended approaches, and selecting the falling sub-point data corresponding to the K-th sorting approach as second falling sub-point data, wherein K is an integer which is greater than or equal to 1 and less than or equal to M.

In a possible implementation manner, the obtaining second point data of the current step drop recommended by the intelligent player according to the board data before the current step drop of the playing player includes: and the intelligent chess players recommend second piece falling point data for generating the current piece falling in the chess steps according to the historical optimal chess power level and the chessboard data of the chess players before the current piece falling in the chess steps.

In one possible implementation manner, the adjusting the initial playing force level of the intelligent player in the current playing game according to the game information of the last game of the playing player includes: determining the chess force level of the playing player according to the chess game information of the last chess game of the playing player; and adjusting the initial chess force level of the intelligent chess players of the current chess game according to the chess force level of the playing players.

In one possible implementation manner, the determining the playing level of the playing player according to the game information of the last game of the playing player includes: respectively determining second sub-point data recommended by the intelligent chess players corresponding to the first sub-point data of each chess step of the playing players; determining the difference between the running method of the first sub-point data of each chess step of the players playing the chess and the corresponding running method of the second sub-point data recommended by the intelligent chess players; and determining the chess force level of the playing player according to the difference of the first sub-point data of each chess step of the playing player.

In one possible implementation manner, the adjusting the initial playing force level of the intelligent player in the current playing game according to the game information of the last game of the playing player includes: under the condition that the game information of the last game is a playing balance, improving the initial playing force level of the intelligent players of the current game; or, under the condition that the game information of the last game is the game of winning the game of the playing player, the initial game force level of the intelligent players of the current game is improved; or, in the case that the game information of the last game is that the playing player is lost, the initial game level of the intelligent player of the current game is lowered.

According to an aspect of the present disclosure, there is provided a human-computer playing device including: the first obtaining module is used for obtaining first piece-falling point data of the current piece falling of the current chess step of the player playing in the current chess game; the second obtaining module is used for obtaining second falling point data of the current chess step falling piece recommended and generated by the intelligent chess player according to the chessboard data before the current chess step falling piece; and the adjusting module is used for adjusting the chess force level of the intelligent chess player according to the first and second piece-dropping point data, and the chess force level determines the third piece-dropping point data of the intelligent chess player for dropping the next chess step.

In one possible implementation, the apparatus further includes: and the global adjustment module is used for adjusting the initial chess force level of the intelligent chess player in the current chess game according to the chess game information of the last chess game of the playing player before the current chess game is played, wherein the chess game information comprises the playing process of the playing player recorded by the intelligent chess player in the interactive playing process.

In one possible implementation, the adjusting module is configured to: determining a difference between the moving method of the first falling point data and the moving method of the second falling point data according to the first falling point data and the second falling point data, wherein the difference represents a difference of levels of good and bad chess force levels between the moving methods; and determining the chess force level of the intelligent chess player corresponding to the next chess step according to the difference and the initial chess force level.

In one possible implementation, the adjusting module is configured to: determining a difference between the moving method of the first falling point data and the moving method of the second falling point data according to the first falling point data and the second falling point data, wherein the difference represents a difference of levels of good and bad chess force levels between the moving methods; and determining the chess strength level of the intelligent chess player corresponding to the step falling of the next chess step according to the difference and the chess strength level of the intelligent chess player corresponding to the step falling of the previous chess step.

In a possible implementation manner, the second obtaining module is configured to: according to the chessboard data of the players before the current chess step is dropped, the intelligent chess players generate M recommended walking methods corresponding to the current chess step; and sorting the M recommended approaches, and selecting the falling sub-point data corresponding to the K-th sorting approach as second falling sub-point data, wherein K is an integer which is greater than or equal to 1 and less than or equal to M.

In a possible implementation manner, the second obtaining module is configured to: and the intelligent chess players recommend and generate second piece falling point data of the current chess step falling according to the historical optimal chess force level and the chessboard data before the current chess step falling of the players.

In one possible implementation, the global adjustment module is configured to: determining the chess force level of the playing player according to the chess game information of the last chess game of the playing player; and adjusting the initial chess force level of the intelligent chess players of the current chess game according to the chess force level of the playing players.

In one possible implementation, the global adjustment module is configured to: under the condition that the game information of the last game is a playing balance, improving the initial playing force level of the intelligent players of the current game; or, under the condition that the game information of the last game is the game of winning the game of the playing player, the initial game force level of the intelligent players of the current game is improved; or, in the case that the game information of the last game is that the playing player is lost, the initial game level of the intelligent player of the current game is lowered.

According to an aspect of the present disclosure, there is provided an electronic device including: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to invoke the memory-stored instructions to perform the above-described method.

According to an aspect of the present disclosure, there is provided a computer readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the above-described method.

In the embodiment of the disclosure, the first piece-falling point data of the current piece falling of the current chess step of the player playing in the current chess game can be obtained; acquiring second drop point data of the current chess step drop recommended by the intelligent chess player according to the chessboard data of the chess player before the current chess step drop; and adjusting the chess force level of the intelligent chess player according to the first piece point data and the second piece point data, wherein the chess force level determines the third piece point data of the intelligent chess player for the next step.

Through this kind of mode, at the in-process of playing of each chess game, through the method of walking of the every step of chess of the player of analyzing the chess and the method of walking of this chess that intelligent chess hand speculates, can progressively dynamically regulated the chess power level that intelligent chess hand played, make intelligent chess hand also can dynamically match player's chess power level in the chess game, be favorable to realizing that intelligent chess hand and the player of playing are balanced, the flag drum is played fairly.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. Other features and aspects of the present disclosure will become apparent from the following detailed description of exemplary embodiments, which proceeds with reference to the accompanying drawings.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the disclosure.

Fig. 1 shows a flowchart of a man-machine playing method according to an embodiment of the present disclosure.

Fig. 2 shows a block diagram of a human-machine playing device according to an embodiment of the present disclosure.

Fig. 3 illustrates a block diagram of an electronic device 800 in accordance with an embodiment of the disclosure.

Fig. 4 shows a block diagram of an electronic device 1900 according to an embodiment of the disclosure.

Detailed Description

Various exemplary embodiments, features and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. In the drawings, like reference numbers can indicate functionally identical or similar elements. While the various aspects of the embodiments are presented in drawings, the drawings are not necessarily drawn to scale unless specifically indicated.

The word "exemplary" is used exclusively herein to mean "serving as an example, embodiment, or illustration. Any embodiment described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments.

The term "and/or" herein is merely an association describing an associated object, meaning that three relationships may exist, e.g., a and/or B, may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the term "at least one" herein means any one of a plurality or any combination of at least two of a plurality, for example, including at least one of A, B, C, and may mean including any one or more elements selected from the group consisting of A, B and C.

Furthermore, in the following detailed description, numerous specific details are set forth in order to provide a better understanding of the present disclosure. It will be understood by those skilled in the art that the present disclosure may be practiced without some of these specific details. In some instances, methods, means, elements and circuits that are well known to those skilled in the art have not been described in detail so as not to obscure the present disclosure.

In the related art, the intelligent players are set to be AI engines with multiple groups of chess force, and after a complete game of chess is finished, whether the current player wins the AI engine with the current chess force can be judged to judge the capability of the current player. Most cases require at least three plays to determine the level of playing force of the players currently participating in the game.

In this case, in the related art, the judgment of the game force of one player requires three or more complete games to judge the game force level of the player who plays the game through the win ratio, and this method of determining the game force level in stages (the whole game) has a certain deficiency in judging the game force of one player who plays the game, and cannot realize the game regulation equivalent to the game force level of the player who plays the game in the game process. The chess power level of the intelligent chess players is adjusted according to the chess playing conditions of different whole games, the adjusted chess power level of the intelligent chess players is fixed in the whole chess games, and the chess power level cannot be dynamically adjusted in the chess games.

In view of this, the man-machine playing method provided by the embodiments of the present disclosure may obtain first drop point data of a current step drop of a player playing in a current game; acquiring second drop point data of the current chess step drop recommended by the intelligent chess player according to the chessboard data of the chess player before the current chess step drop; and adjusting the chess force level of the intelligent chess player according to the first piece point data and the second piece point data, wherein the chess force level determines the third piece point data of the intelligent chess player for the next step.

Through the mode, in the playing process of each chess game, the chess force level of the intelligent chess player can be dynamically adjusted step by analyzing the walking method of each step of the chess step of the player playing chess and the walking method of the chess step presumed by the intelligent chess player, so that the intelligent chess player can also dynamically match the chess force level of the player in the chess game, and the intelligent chess player and the player playing chess can be favorably balanced and equivalently played on a flag drum.

Fig. 1 shows a flowchart of a man-machine game playing method according to an embodiment of the present disclosure, which may be performed by an electronic device such as a terminal device (UE), a mobile device, a User terminal, a cellular phone, a cordless phone, a Personal Digital Assistant (PDA), a handheld device, a computing device, an in-vehicle device, a wearable device, or the like, or a server, and the method may be implemented by a processor calling computer-readable instructions stored in a memory. Alternatively, the method may be performed by a server.

As shown in fig. 1, the man-machine playing method includes:

in step S1, first piece-dropping point data of the current piece of the player' S current step in the current game is acquired.

In step S2, second point data of the current step generated by the intelligent player recommended by the board data before the current step of the playing player is obtained.

In step S3, the playing force level of the intelligent player is adjusted according to the first and second playing point data, and the playing force level determines the third playing point data of the next playing step of the intelligent player.

In one possible implementation, the chess types played by the human-computer game may include chess, go, military chess, gobang, checkers, toilet chess, footroom chess, twitch chess, strategy chess, tiger-driving chess, flying chess, and the like, and the present disclosure does not limit the chess types played by the human-computer interaction.

In one possible implementation, the manner of human-machine playing may be online human-machine playing or online human-machine playing, which is not limited by this disclosure.

The online human-computer chess playing is that a person directly plays chess on a chessboard of a physical world with an intelligent chess player (for example, a robot device), the chess player can directly perform a step-down action on the chessboard, the intelligent chess player can recognize the step of the chess player through at least one chessboard image shot by an image acquisition device (for example, a camera), and the intelligent chess player can also control a mechanical arm of the intelligent chess player to perform the step-down action on the chessboard according to the step-down of the chess player.

The online man-machine chess playing is that a player and an intelligent chess player (such as a software program) play chess on an electronic device (such as a mobile phone, a computer and the like), the player playing chess can perform a falling stroke through a mouse, a keyboard, a touch screen, voice input and the like, and the intelligent chess player can display the falling stroke of the next chess step on a display interface (such as a screen) of the electronic device after receiving the falling stroke of the player playing chess. The concrete mode of playing the human-computer is not limited in this disclosure.

In one possible implementation, the first landing point data obtained in step S1 may be used to record the position of the current step of the playing player on the board.

For the online human-computer game scene, the intelligent chess players can perform image recognition on at least one frame of chessboard image shot by an image acquisition device (such as a camera), and determine first drop point data of the current step drop of the players playing in the current chess game. For example, the first piece falling point data of the current piece in the chess step can be identified by analyzing the difference between the chessboard image of the current piece in the chess step and the chessboard image of the previous piece in the chess step; alternatively, the first drop point data of the current chess step drop may be determined by analyzing at least one board image (e.g., a frame image containing information on the motion of the playing player's drop) of the current chess step drop, identifying the drop motion of the playing player's current chess step drop, and determining the first drop point data of the current chess step drop.

For the online human-computer game scene, players can fall through a mouse, a keyboard, a touch screen, voice input and other modes, the position of the current chess step in the chessboard can be determined according to the falling operation of the players, and first falling sub point data of the current chess step of the players is obtained.

In one possible implementation, in step S2, the intelligent player may recommend generating second drop point data corresponding to the current step drop based on the board data of the player before the current step drop.

The intelligent players may be preset playing programs or software, pre-trained machine learning models with playing functions, artificial intelligence engines, or hardware devices (such as terminal devices, servers, etc.) loaded with the playing programs or software. The chessboard data is used for recording the distribution state of all chessmen existing on the chessboard. The second falling point data is used for recording the position of the falling point on the chessboard corresponding to the recommended moving method of the current chess step, which is estimated from the standpoint (or the role) of the player playing the chess, and can be used as comparison data for evaluating the playing force level of the moving method of the first falling point data of the player playing the chess.

It should be understood that the present disclosure does not limit the sequential logic order of step S1 and step S2, and step S1 may be executed first to obtain the first falling sub-point data, and then step S2 may be executed to obtain the second falling sub-point data; or step S2 may be executed first to obtain the second falling point data, and step S1 may be executed to obtain the first falling point data; step S1 and step S2 may also be executed in parallel, and the first falling-point data and the second falling-point data are synchronously acquired.

In a possible implementation manner, the first and second fall point data are obtained in steps S1-S2, and in step S3, the playing force level of the intelligent player is adjusted according to the first and second fall point data, so that the intelligent player determines the third fall point data of the next playing step according to the adjusted playing force level.

The third piece-falling point data is used for recording the position of the intelligent player falling piece on the chessboard of the next piece, namely the piece of the intelligent player and the piece of the player who plays the chess in the position (role) of confronting the player who plays the chess.

The chess force level can be a parameter or coefficient used for representing the professional degree of playing chess by the intelligent chess player. For example, assuming that the parameter C represents the chess power level, the larger the value of the parameter C is, the more optimal the chess power level is, and the value range of the parameter C may be C e [ C ∈ [ ]_min，C_max]. For example, an intelligent player may determine the moves of a drop that matches the force level according to different values of C (i.e., different force levels). It should be understood that the value range of the chess force level C can be set according to actual scenes, and the disclosure does not limit this.

For ease of understanding, steps S1 to S3 will be described below with go and chess as examples.

For example, for go, the player may play the game by using a rectangular grid-shaped chessboard and black-white circular chessmen, and if the player plays white chess, the player plays black chess, and the player turns to play the game in the nth step, the first falling point data of the falling piece of the current step of the player, that is, the coordinate of the position of the falling piece of the white chess in the nth step in the chessboard, may be obtained in step S1.

Synchronously, in step S2, the intelligent player may determine a second piece-dropping point data of the current step (nth step) of the white chess piece according to the board data before the step N of the white chess piece (i.e., after the step N-1 of the black chess piece), which may be a recommended walk inferred by the intelligent player from the standpoint (or role) of the white chess party based on the optimal board force level.

In step S3, the difference between the running of the player' S step and the predicted step running of the intelligent player is obtained based on the running of the first fall point data of the white chess at the nth step of the player who played and the running of the second fall point data of the white chess at the nth step measured by the intelligent player, and the difference is used to adjust the force level of the intelligent player, so that the intelligent player can determine the third fall point data of the black chess at the next step (i.e., the (N +1) th step) according to the adjusted force level.

For example, in the case of a chess, a square-shaped checkered board is used, a blank zone in which no vertical line is drawn between the fifth and sixth horizontal lines of the board is called a "river boundary", the chess is played by red and black circular pieces, commanders, cars, horses, cannons, phases, soldiers and soldiers are marked on the red pieces, and commanders, cars, horses, cannons, elephants, soldiers and pawns are marked on the black pieces.

Assuming that the playing player carries out the red chess, the intelligent chess player holds the black chess, and the nth step takes turns until the playing player plays, in step S1, the first falling point data of the falling piece of the current chess step (nth step) of the playing player can be obtained. Considering that in chess, different pieces represent different functions (e.g., horse-march, elephant-flying field), the first drop point data may include an identification of the red chess drop for the nth step and coordinates in the board corresponding to the identified red chess drop position.

Synchronously, in step S2, the intelligent player may determine a second piece-dropping point data of the current step (nth step) of the chess piece according to the chessboard data before the step N of the chess piece is dropped (i.e. after the step N-1 of the black chess piece is dropped), where the second piece-dropping point data may be the position (or role) of the intelligent player in the chess piece based on the recommended walking method estimated based on the optimal chess force level, and the second piece-dropping point data may include the identifier of the step N estimated chess piece and the coordinates of the position of the identified chess piece on the chessboard.

In step S3, the difference between the step of the player and the step presumed by the intelligent player can be obtained according to the step of the first piece point data of the red chess in the nth step of the player and the step of the second piece point data of the red chess in the nth step measured by the intelligent player, and the difference can be used to adjust the force level of the intelligent player, and the intelligent player can determine the third piece point data of the black chess in the next step (i.e., step N +1) according to the adjusted force level, where the third piece point data can include the black chess identifier and the coordinates of the black chess piece position in the chessboard corresponding to the identifier.

The above go and chess are only taken as examples, the present disclosure does not limit the chess, and other chess can refer to the above text in the man-machine playing process of the current chess, and the method for dynamically adjusting the chess force level of the current chess is not described herein again.

It should be appreciated that the above steps may be alternately cycled to adaptively dynamically adjust the force level of the intelligent player.

Through the steps S1-S3, in the playing process of each game, the playing force level of the intelligent players can be dynamically adjusted step by analyzing the playing method of each step of the playing players and the playing method of the step presumed by the intelligent players, so that the intelligent players can dynamically match the playing force level of the players in the game, and the intelligent players and the flag drums can play equally well.

The embodiment of the disclosure not only can dynamically adjust the playing force level of the intelligent players during the playing process of each game, but also can globally adjust the playing force level of the intelligent players according to the game information.

In a possible implementation manner, before the current game is played, the initial playing force level of the intelligent player in the current game is adjusted according to the game information of the last game of the playing player, wherein the game information comprises the playing process of the playing player recorded by the intelligent player in the interactive playing process.

Before the man-machine interaction of the current chess game is played, if the intelligent chess player and the playing player do not play chess, the preset chess power level can be determined as the initial chess power level of the intelligent chess player. The preset chess force level can be a default value of the system, can be set according to the chess force of most users, can be randomly set, and can also be a maximum value, a minimum value or an average value in a chess force level value range, and the preset chess force level is not limited by the disclosure.

If the intelligent chess player plays chess with the player playing chess, the intelligent chess player records the playing process of the player playing chess, and the initial chess force level of the intelligent chess player of the current chess game, namely the initial difficulty of the current chess game, can be adjusted according to the chess game information of the last chess game of the player playing chess.

The game information may include playing processes of the playing players recorded by the intelligent players in the process of human-computer interaction playing, and may include, for example, data of a playing point of each playing step of the playing players, chessboard data before each playing step of the playing players, and winning and losing result information of playing.

Through the mode, the initial chess power level of the intelligent chess players in the current chess game can be adjusted according to the chess game information of the last chess game of the chess players, the whole-game adjustment of the chess power level of the intelligent chess players playing is realized, and the intelligent chess players and the players can play the chess in a balanced manner.

In one possible implementation, in a case that the game information of the last game is a playing balance, the initial playing force level of the intelligent player of the current game is increased; or, under the condition that the game information of the last game is the game of winning the game of the playing player, the initial game force level of the intelligent players of the current game is improved; or, in the case that the game information of the last game is that the playing player is lost, the initial game level of the intelligent player of the current game is lowered.

For example, for a scenario where a professional chess player (a playing player) and an intelligent chess player play go, it is assumed that a value range of the chess force level C may be [0,9 ], wherein the chess force level C e [0,1) may correspond to a professional initial segment, the chess force level C e [1,2) may correspond to a professional second segment, the chess force level C e [2,3) may correspond to a professional third segment, the chess force level C e [3,4) may correspond to a professional fourth segment, the chess force level C e [4,5) may correspond to a professional fifth segment, the chess force level C e [5,6) may correspond to a professional sixth segment, the chess force level C e [6,7) may correspond to a professional seventh segment, the chess force level C e [7,8) may correspond to a professional eighth segment, and the chess force level C e [8,9) may correspond to a professional segment.

If the K-1 game (the last game) is a balance between the professional player and the intelligent player, the game force levels of the professional player and the intelligent player are equivalent, and under the condition, the initial game force level of the intelligent player in the K game can be improved. For example, assuming that the initial playing force level C of the current intelligent player is 1.2, the initial playing force level C of the intelligent player in the K-th game can be increased by 0.1 to 1.3. Wherein, to the scene of tie, can improve intelligent chess player's initial chess power level by a small margin, also promptly in the same section position suitably improve intelligent chess player's initial chess power level C for the initial chess power level of intelligent chess player after the adjustment is in same section position with the player of playing chess.

If the K-1 game defeats the intelligent chess player for the professional chess player, the chess game is won, the chess power level of the professional chess player is higher than that of the intelligent chess player, and under the condition, the initial chess power level of the intelligent chess player of the K game can be improved. And, the increasing range of the initial chess force level of the intelligent chess player in the K game can be determined according to the chess game information of the K-1 game. In the case that the game information of the K-1 th game indicates that the professional players win the game with a smaller number of game steps, the initial game force level C of the intelligent player of the K-th game may be substantially increased (e.g., including a step increase), for example, assuming that the initial game force level C of the current intelligent player is 1.2, 1 may be increased, so that the initial game force level C of the intelligent player of the K-th game is 2.2. Under the condition that the game information of the K-1 game indicates that the professional players win the game with more game steps, the initial game force level of the intelligent players of the K game can be improved (for example, improved in a section) in a small range, for example, the initial game force level of the current intelligent players is 1.2, and can be improved by 0.3, so that the initial game force level C of the intelligent players of the K game is 1.5.

If the K-1 th game defeats the professional players for the intelligent players, the game is won, and the game force level of the professional players is lower than that of the intelligent players, so that the initial game force level of the intelligent players in the K-th game can be reduced. And, the reduction amplitude of the initial chess force level of the intelligent chess player of the K game can be determined according to the chess game information of the K-1 game. In the case where the game information of the K-1 th game indicates that the professional players have taken the game with a small number of game steps, the initial game force level C of the intelligent player of the K-th game may be substantially reduced (e.g., including a step reduction), for example, assuming that the current initial game force level C of the intelligent player is 2.2, 1 may be reduced, so that the initial game force level C of the intelligent player of the K-th game is 1.2. In the case that the game information of the K-1 th game indicates that the professional players have entered the game with a large number of game steps, the initial game force level of the intelligent player of the K-th game may be reduced by a small amount (e.g., reduced in a section), for example, assuming that the initial game force level of the current intelligent player is C1.2, 0.2 may be reduced, so that the initial game force level C of the intelligent player of the K-th game is 1.

It should be understood that the present disclosure is not limited to the initial chess force level being the magnitude of the C value, and the particular magnitude of the increase or decrease, and that the C value above is exemplary only.

Through the mode, the chess force level of the intelligent chess players playing chess can be regulated in a whole place according to the chess game information of the last chess game of the chess players, and the intelligent chess players and the players can play chess in a balanced manner.

The man-machine playing method of the embodiment of the present disclosure will be explained below in terms of dynamically adjusting the playing force level of the intelligent player based on the whole chessboard chess game and dynamically adjusting the playing force level of the intelligent player based on the chess steps in the chess game.

Before the current chess game is played, the initial chess force level of the intelligent chess players in the current chess game can be adjusted according to the information of the last chess game of the players.

In one possible implementation, the playing force level of the playing player can be determined according to the game information of the last game of the playing player; and adjusting the initial chess force level of the intelligent chess players of the current chess game according to the chess force level of the playing players.

For example, the game information includes all of the first sub-point data of the last game of the playing player, and the determining the playing force level of the playing player according to the game information of the last game of the playing player includes: respectively determining second sub-point data recommended by the intelligent chess players corresponding to the first sub-point data of each chess step of the playing players; determining the difference between the running method of the first sub-point data of each chess step of the players playing the chess and the corresponding running method of the second sub-point data recommended by the intelligent chess players; and determining the chess force level of the playing player according to the difference of the first sub-point data of each chess step of the playing player.

For example, assuming that the player who played the chess performs white chess, the intelligent player performs black chess, assuming that the player who played the chess performs white chess in the first step first, the first piece-falling point data X1 is recorded (synchronously, the intelligent player can determine the recommended first piece-falling piece of white chess based on the historical optimal chess force level, the second piece-falling point data Y1 is recorded), the intelligent player who played the chess performs black chess, the third piece-falling piece of white chess, the first piece-falling point data X2 is recorded (synchronously, the intelligent player can determine the recommended third piece-falling piece of white chess based on the historical optimal chess force level, the second piece-falling point data Y2 is recorded, the intelligent player who played the fourth step performs black chess, and so on, until the player who played the third step performs white chess, the first piece-falling piece X [ (P +1)/2] is recorded (synchronously, the intelligent player can determine the recommended third piece-falling piece of white chess based on the historical optimal chess force level, and recording the second fall point data Y [ (P +1)/2]), and ending the game. In the process, players play white chess in a (P +1)/2 chess steps, and the chess game information of the chess game comprises first falling sub point data X [1] -X [ (P +1)/2] of the (P +1)/2 players and second falling sub point data Y [1] -Y [ (P +1)/2] recommended by the corresponding (P +1)/2 intelligent players.

Determining the difference 1 between the first sub-point data X1 of the players and the second sub-point data Y1 recommended by the intelligent players; determining the difference 2 between the first sub-point data X2 of the players and the second sub-point data Y2 recommended by the intelligent players; and determining the difference (P +1)/2 of the playing force level between the playing of the first playing point data X [ (P +1)/2] of the playing player and the playing of the second playing point data Y [ (P +1)/2] recommended by the intelligent player by analogy.

The difference 1-difference (P +1)/2 can be comprehensively analyzed, and the playing force level of the players can be determined. For example, the second sub-point data recommended by the intelligent chess player corresponds to the optimal chess effort level of C_max. The average difference C' (or the maximum difference, the minimum difference and the like) obtained according to the historical difference 1 to the historical difference (P +1)/2 can be utilized to determine the chess force level of the players, namely: c_max-C’。

Through the mode, the information of all the chess steps of the playing players in the chess game is comprehensively considered, and the chess force level of the playing players can be determined more accurately.

The chess force level of the players who play chess is determined according to the chess game information of the last chess game of the players who play chess, and the initial chess force level of the intelligent players who play chess can be adjusted according to the chess force level of the players who play chess.

The chess power level of the players playing chess can be directly determined as the initial chess power level of the intelligent chess players, the chess power level of the players playing chess can be subjected to mathematical operation (such as addition, multiplication and the like) with a certain preset coefficient, and the operation result is determined as the initial chess power level of the intelligent chess players.

Through the mode, the chess force level of the intelligent chess player playing chess can be adjusted according to the chess game information of the last chess game of the chess playing player under the condition that the information of all chess steps of the chess playing player in the last chess game is comprehensively considered, and the intelligent chess player and the player can play chess in a balanced manner.

The following introduces a chess force level which can be dynamically adjusted by an intelligent chess player based on chess steps in a chess game.

In steps S1 to S2, first point data of the current step of the playing player may be acquired (step S1), and second point data of the current step recommended by the intelligent player may be acquired based on the board data of the playing player before the current step of the playing player (step S2).

In one possible implementation, step S2 may include: according to the chessboard data of the players before the current chess step is dropped, the intelligent chess players generate M recommended walking methods corresponding to the current chess step; and sorting the M recommended approaches, and selecting the falling sub-point data corresponding to the K-th sorting approach as second falling sub-point data, wherein K is an integer which is greater than or equal to 1 and less than or equal to M.

For example, assuming that the player who plays the game performs white chess, the intelligent player performs black chess, and the player who plays the game goes N times, the chessboard data before the N-th step of white chess is played (i.e., the chessboard data after the N-1-th step of black chess is played) can be obtained, and the chessboard data can include the distribution state of all the chess pieces existing on the chessboard, for example, the position coordinate corresponding to each white chess, the position coordinate corresponding to each black chess, and the position coordinate of each free position on the chessboard.

Can utilize intelligent chess player's software interface, go chess player N step before the white chess piece falls chess data input to intelligent chess player to make intelligent chess player carry out data analysis to this chess board data, obtain M kinds of methods of walking of N chess step white chess piece (every kind of method of walking can correspond a white chess piece coordinate of falling chess), and according to the good and bad sequencing of walking of M kinds of methods of chess power level, promptly: the move 1 to move M, in which the move 1(K ═ 1) as the top sort has the highest chess force level, and the move M as the last sort has the lowest chess force level.

The intelligent chess player can select the falling point of one walking method (the K-th ranking walking method) from the M walking methods as the second falling point data of the falling point of the N-th step white chess, and the second falling point data can be used as a reference standard for evaluating the chess strength level corresponding to the N-th step white chess of the player playing chess.

In the case of improving the chess power level of the intelligent chess player, the piece dropping point data corresponding to the first-ranked moving 1(K ═ 1) can be selected as the second piece dropping point data, wherein the chess power level corresponding to the moving 1 is the optimal chess power level.

The increase or decrease of the playing force level of the intelligent players can be determined according to the identity or playing force level of the players playing. The intelligent chess players can directly acquire the identities or chess strength levels of the players playing chess through the man-machine interaction interface of the chess playing system, namely the identities or chess strength levels of the players playing chess can be acquired through inquiring or analyzing historical data.

For example, in practical applications, if the player who plays the game is a professional player, the playing force level of the first piece-dropping point data of the previous piece (each piece of the 1 st to N-1 st steps that the player who plays the game goes to put the white chess) of the player is higher or mostly higher than the corresponding second piece-dropping point data, so that the playing force level of the intelligent player can be increased, the piece-dropping point data corresponding to the first-order playing method 1 of the intelligent player can be used as the second piece-dropping point data, so that the second piece-dropping point data for evaluating the playing force level of the player is at the optimal level, which is beneficial to evaluating the playing force level of the player more efficiently, and is further beneficial to dynamically adjusting the subsequent playing force level of the intelligent player.

Alternatively, when the chess force level of the intelligent chess player is reduced, the falling point data corresponding to the walking method (for example, walking method 2, K is 2) with no top ranking is selected as the second falling point data, wherein the lower the ranking of the selected walking method, the larger the reduction range of the chess force level of the intelligent chess player.

For example, in an actual application, if the player who plays the game is a maiden player, the game force level of the first piece-dropping point data of the previous game step of the player (each game step of the 1 st to N-1 st steps where the player who plays the white game moves) is lower than or mostly lower than the corresponding second piece-dropping point data, the game force level of the intelligent player can be lowered, and the piece-dropping point data corresponding to the move with a relatively backward ranking (for example, move 5) can be used as the second piece-dropping point data, so that the second piece-dropping point data for evaluating the game force level of the player who plays the game is close to the game force level of the player who plays the game, which is advantageous for evaluating the game force level of the player more efficiently, and is further advantageous for dynamically adjusting the game force level of the intelligent player subsequently.

Through the mode, the second piece falling point data can be determined according to the walking method recommended by the intelligent player for the current chess step falling, so that the chess strength level of the players playing chess can be more efficiently evaluated, and the subsequent dynamic adjustment of the chess strength level of the intelligent player can be further facilitated.

In one possible implementation, step S2 may include: and the intelligent chess players recommend and generate second piece falling point data of the current chess step falling according to the historical optimal chess force level and the chessboard data before the current chess step falling of the players.

For example, the intelligent player may include a neural network model that is obtained after training the neural network using the playing data of the top player and that can be recommended by the player for walking based on the optimal playing force level, and the board data of the playing player before the current step is dropped may be input into the neural network model, and the second drop point data of the current step is output.

In this way, the intelligent players can always recommend the second sub-point data at the optimal playing force level, so that the reference standard for evaluating the playing force level of the players is always at the optimal level.

After the first and second drop point data are obtained in steps S1-S2, the player may adjust the player force level of the intelligent player according to the first and second drop point data in step S3, and the intelligent player may determine the third drop point data of the next player step according to the adjusted player force level.

In one possible implementation, the chess step S3 may include:

in step S31, determining a difference between the movement of the first drop point data and the movement of the second drop point data according to the first drop point data and the second drop point data, where the difference represents a difference between the good and bad levels of the playing force level between the movements;

for example, the first and second landing point data may be input into a pre-trained neural network model, and the difference between the walk of the first landing point data and the walk of the second landing point data may be determined by comparing the chess power levels of the walk of the first landing point data and the walk of the second landing point data by the neural network model. And all recommended moves can be obtained by the intelligent player, the recommended moves are sorted according to the level of the playing force (the move corresponding to the optimal playing force level can be the move of the second landing point data), and the difference between the move of the first landing point data and the move of the second landing point data can be determined by analyzing the arrangement position of the first landing point data move. The present disclosure does not limit the specific method of determining the difference between the walk of the first drop point data and the walk of the second drop point.

The difference between the walk of the first drop point data and the walk of the second drop point data is determined in step S31, and the playing force level of the intelligent player corresponding to the next step can be determined according to the difference and the initial playing force level in step S32.

For example, assuming that the initial chess force level of the current chess game is C [0], the chess force level C [ N +1] of the intelligent chess player corresponding to the step of the next chess step (step N +1) can be determined according to the initial chess force level C [0] and the difference P [ N ] between the steps of the first step point data and the second step point data of the current chess step (step N), namely: c [ N +1] + C [0] + P [ N ].

The method of steps S31-S32 can be applied to the scene that the intelligent chess player and the playing player play games for the amateur chess player. Because the chess power level of the amateur chess players is relatively stable, the chess power level of the amateur chess players in a short time cannot change too much, the difference between the playing method of the chess steps of the chess players (first piece point data) and the chess step playing method presumed by the intelligent chess players (second piece point data) can be utilized, the initial chess power level is utilized, the chess power level of the intelligent chess players is adjusted, the chess power level of the intelligent chess players can float up and down on the initial chess power level, the application scene that the chess power level of the chess players is relatively stable is suitable, the evaluation of the chess power level of the chess players in the chess playing process can be realized, the refined chess power level of the intelligent chess players is adjusted by utilizing the result of evaluating the chess power, and the balance of the game is realized.

Alternatively, the difference between the walk of the first drop point data and the walk of the second drop point data is determined in step S31, and the board power level of the intelligent player corresponding to the drop of the next board step may be determined according to the difference and the board power level of the intelligent player corresponding to the previous board step in step S32'.

For example, the chess force level C [ N +1] of the intelligent chess player corresponding to the step dropped in the next chess step (step N +1) can be adjusted according to the chess force level C [ N-1] of the intelligent chess player corresponding to the step dropped in the previous chess step (step N-1) and the difference P [ N ] between the moving method of the first drop point data and the moving method of the second drop point data of the current chess step (step N): c [ N +1] ═ C [ N-1] + P [ N ].

The method of the steps S31-S32' can be applied to the scene that the intelligent chess player and the playing player play games for professional chess players. Under this scene, professional chess player is more sensitive to intelligent chess player's chess power level, and at the in-process of playing chess, the condition that professional chess player's level promoted by a wide margin because of a certain step may exist, utilize the difference between the method of walking of the player's chess step (first chessman data) of playing chess and the intelligent chess step method of walking that intelligent chess player guesses (second chessman data), and the chess power level of the intelligent chess player of last chess step, adjust the chess power level that the intelligent chess player of next chess step fell, this kind of iterative adjustment mode, can the chess power level of the intelligent chess player of self-adaptation adjustment, can make intelligent chess player more high-efficiently match the chess power of the player of playing chess, realize the balanced chess of winning rate.

In a particular application, an artificial intelligence engine may be configured as an intelligent chess player, which may include a first service and a second service. The first service is used for determining a second drop point, wherein the first service can be a neural network model which is obtained after training the neural network by using the playing data of top players and can be recommended by a player on the basis of the optimal playing force level. The first service can be used as a reference standard for evaluating the chess power level of the players playing the chess, the first service can recommend multiple games (each game can correspond to a piece dropping point), relative differences in the chess power level still exist among the multiple games, the first service can sort the multiple games according to the chess power level, and the recommended game of the first sorting can correspond to the optimal chess power level game.

The second service is used to determine a third drop point, i.e. to match the playing level of the playing player to determine the move of the next step. For example, the second service may be a neural network model that can be obtained by training a neural network using playing data of various players (professional/amateur players that may include various segments/levels) and that can perform dropping based on the playing force level.

It should be understood that, in order to implement the human-computer game, the artificial intelligence engine may include not only the first service and the second service, but also other different services for different functions, each of which may be a program, a routine, a process, etc. that performs a specific function, and the disclosure does not limit the number of services included in the artificial intelligence engine.

According to the embodiment disclosed herein, the playing force level of the intelligent players can be locally adjusted according to the game information of the last game of the players playing the game, and in the man-machine game playing process of each game, the playing force level of each step of playing the game of the players playing the game can be evaluated, the intelligent players can recommend corresponding game steps according to the falling piece of each step of playing the game of the players playing the game, the difference between the moving method of each step of the players playing the game and the corresponding moving method presumed by the intelligent players playing the game is analyzed, and the playing force level of the intelligent players is dynamically adjusted by judging the score of the moving piece of each step of the players playing the game. In conclusion, the embodiments of the present disclosure can adaptively estimate the chess force levels of both parties of the chess game and adaptively adjust the chess force levels of the intelligent players according to the chess piece playing methods of both parties of the human-computer chess game, thereby realizing interesting and smooth chess game equivalent to the flag drums of both parties of the chess game.

It is understood that the above-mentioned method embodiments of the present disclosure can be combined with each other to form a combined embodiment without departing from the logic of the principle, which is limited by the space, and the detailed description of the present disclosure is omitted. Those skilled in the art will appreciate that in the above methods of the embodiments, the specific order of execution of the various chess steps should be determined by their function and possibly their inherent logic.

In addition, the present disclosure also provides a man-machine playing device, an electronic device, a computer-readable storage medium, and a program, which can be used to implement any one of the man-machine playing methods provided by the present disclosure, and the corresponding technical solutions and descriptions and corresponding descriptions in the methods section are referred to and are not repeated.

Fig. 2 shows a block diagram of a human-machine playing device according to an embodiment of the present disclosure, which, as shown in fig. 2, includes:

the first obtaining module 21 is configured to obtain first falling point data of a current step falling of a player playing in the current chess game;

a second obtaining module 22, configured to obtain second hit point data of the current hit piece recommended by the intelligent player according to the chessboard data of the playing player before the current hit piece;

and the adjusting module 23 is configured to adjust the chess force level of the intelligent chess player according to the first piece drop point data and the second piece drop point data, where the chess force level determines third piece drop point data of a piece dropped by the intelligent chess player in a next chess step.

In a possible implementation manner, the adjusting module 23 is configured to: determining a difference between the moving method of the first falling point data and the moving method of the second falling point data according to the first falling point data and the second falling point data, wherein the difference represents a difference of levels of good and bad chess force levels between the moving methods; and determining the chess force level of the intelligent chess player corresponding to the next chess step according to the difference and the initial chess force level.

In a possible implementation manner, the adjusting module 23 is configured to: determining a difference between the moving method of the first falling point data and the moving method of the second falling point data according to the first falling point data and the second falling point data, wherein the difference represents a difference of levels of good and bad chess force levels between the moving methods; and determining the chess force level of the intelligent chess hand corresponding to the step of the next chess according to the difference and the chess force level of the intelligent chess hand corresponding to the step of the previous chess.

In a possible implementation manner, the second obtaining module 22 is configured to: according to the chessboard data of the players before the current chess step is dropped, the intelligent chess players generate M recommended walking methods corresponding to the current chess step; and sorting the M recommended approaches, and selecting the falling sub-point data corresponding to the K-th sorting approach as second falling sub-point data, wherein K is an integer which is greater than or equal to 1 and less than or equal to M.

In a possible implementation manner, the second obtaining module 22 is configured to: and the intelligent chess players recommend second piece falling point data for generating the current piece falling in the chess steps according to the historical optimal chess power level and the chessboard data of the chess players before the current piece falling in the chess steps.

In one possible implementation, the global adjustment module is configured to: under the condition that the game information of the last game is a playing balance, improving the initial playing force level of the intelligent players of the current game; or, under the condition that the game information of the last game is the game of winning the game of the playing player, the initial game force level of the intelligent players of the current game is improved; or, in the case that the game information of the previous game is the game passed by the playing player, the initial game level of the intelligent players in the current game is lowered.

The method has specific technical relevance with the internal structure of the computer system, and can solve the technical problems of how to improve the hardware operation efficiency or the execution effect (including reducing data storage capacity, reducing data transmission capacity, improving hardware processing speed and the like), thereby obtaining the technical effect of improving the internal performance of the computer system according with the natural law.

In some embodiments, functions of or modules included in the apparatus provided in the embodiments of the present disclosure may be used to execute the method described in the above method embodiments, and specific implementation thereof may refer to the description of the above method embodiments, and for brevity, will not be described again here.

Embodiments of the present disclosure also provide a computer-readable storage medium having stored thereon computer program instructions, which when executed by a processor, implement the above-mentioned method. The computer readable storage medium may be a volatile or non-volatile computer readable storage medium.

An embodiment of the present disclosure further provides an electronic device, including: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to invoke the memory-stored instructions to perform the above-described method.

The disclosed embodiments also provide a computer program product comprising computer readable code or a non-transitory computer readable storage medium carrying computer readable code, which when run in a processor of an electronic device, the processor in the electronic device performs the above method.

The electronic device may be provided as a terminal, server, or other form of device.

Fig. 3 illustrates a block diagram of an electronic device 800 in accordance with an embodiment of the disclosure. For example, the electronic device 800 may be a User Equipment (UE), a mobile device, a User terminal, a cellular phone, a cordless phone, a Personal Digital Assistant (PDA), a handheld device, a computing device, a vehicle-mounted device, a wearable device, or other terminal device.

Referring to fig. 3, electronic device 800 may include one or more of the following components: a processing component 802, a memory 804, a power component 806, a multimedia component 808, an audio component 810, an input/output (I/O) interface 812, a sensor component 814, and a communication component 816.

The processing component 802 generally controls overall operation of the electronic device 800, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing components 802 may include one or more processors 820 to execute instructions to perform all or a portion of the steps of the methods described above. Further, the processing component 802 can include one or more modules that facilitate interaction between the processing component 802 and other components. For example, the processing component 802 can include a multimedia module to facilitate interaction between the multimedia component 808 and the processing component 802.

The memory 804 is configured to store various types of data to support operations at the electronic device 800. Examples of such data include instructions for any application or method operating on the electronic device 800, contact data, phonebook data, messages, pictures, videos, and so forth. The memory 804 may be implemented by any type or combination of volatile or non-volatile memory devices such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disks.

The power supply component 806 provides power to the various components of the electronic device 800. The power components 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the electronic device 800.

The multimedia component 808 includes a screen that provides an output interface between the electronic device 800 and a user. In some embodiments, the screen may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive an input signal from a user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundary of a touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 808 includes a front facing camera and/or a rear facing camera. The front camera and/or the rear camera may receive external multimedia data when the electronic device 800 is in an operation mode, such as a shooting mode or a video mode. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.

The audio component 810 is configured to output and/or input audio signals. For example, the audio component 810 includes a Microphone (MIC) configured to receive external audio signals when the electronic device 800 is in an operational mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals may further be stored in the memory 804 or transmitted via the communication component 816. In some embodiments, audio component 810 also includes a speaker for outputting audio signals.

The I/O interface 812 provides an interface between the processing component 802 and peripheral interface modules, which may be keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to: a home button, a volume button, a start button, and a lock button.

The sensor assembly 814 includes one or more sensors for providing various aspects of state assessment for the electronic device 800. For example, the sensor assembly 814 may detect an open/closed state of the electronic device 800, the relative positioning of components, such as a display and keypad of the electronic device 800, the sensor assembly 814 may also detect a change in the position of the electronic device 800 or a component of the electronic device 800, the presence or absence of user contact with the electronic device 800, orientation or acceleration/deceleration of the electronic device 800, and a change in the temperature of the electronic device 800. Sensor assembly 814 may include a proximity sensor configured to detect the presence of a nearby object without any physical contact. The sensor assembly 814 may also include a light sensor, such as a Complementary Metal Oxide Semiconductor (CMOS) or Charge Coupled Device (CCD) image sensor, for use in imaging applications. In some embodiments, the sensor assembly 814 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

The communication component 816 is configured to facilitate wired or wireless communication between the electronic device 800 and other devices. The electronic device 800 may access a wireless network based on a communication standard, such as a wireless network (Wi-Fi), a second generation mobile communication technology (2G), a third generation mobile communication technology (3G), a fourth generation mobile communication technology (4G), a long term evolution of universal mobile communication technology (LTE), a fifth generation mobile communication technology (5G), or a combination thereof. In an exemplary embodiment, the communication component 816 receives a broadcast signal or broadcast related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 816 further includes a Near Field Communication (NFC) module to facilitate short-range communications. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, Ultra Wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

In an exemplary embodiment, the electronic device 800 may be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, micro-controllers, microprocessors or other electronic components for performing the above-described methods.

In an exemplary embodiment, a non-transitory computer-readable storage medium, such as the memory 804, is also provided that includes computer program instructions executable by the processor 820 of the electronic device 800 to perform the above-described methods.

Fig. 4 shows a block diagram of an electronic device 1900 according to an embodiment of the disclosure. For example, the electronic device 1900 may be provided as a server or terminal device. Referring to fig. 4, electronic device 1900 includes a processing component 1922 further including one or more processors and memory resources, represented by memory 1932, for storing instructions, e.g., applications, executable by processing component 1922. The application programs stored in memory 1932 may include one or more modules that each correspond to a set of instructions. Further, the processing component 1922 is configured to execute instructions to perform the above-described method.

The electronic device 1900 may further include a power component 1926 configured to perform power management of the electronic device 1900, and a wired or wireless network interface 1950 configuredFor connecting the electronic device 1900 to a network, and an input/output (I/O) interface 1958. The electronic device 1900 may operate based on an operating system, such as the Microsoft Server operating system (Windows Server), stored in the memory 1932^TM) Apple Inc. of the present application based on the graphic user interface operating System (Mac OS X)^TM) Multi-user, multi-process computer operating system (Unix)^TM) Free and open native code Unix-like operating System (Linux)^TM) Open native code Unix-like operating System (FreeBSD)^TM) Or the like.

In an exemplary embodiment, a non-transitory computer readable storage medium, such as the memory 1932, is also provided that includes computer program instructions executable by the processing component 1922 of the electronic device 1900 to perform the above-described methods.

The present disclosure may be systems, methods, and/or computer program products. The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied thereon for causing a processor to implement various aspects of the present disclosure.

The computer readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: 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), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as punch cards or in-groove projection structures having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or electrical signals transmitted through electrical wires.

The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or to an external computer or external storage device via a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing/processing device.

The computer program instructions for carrying out operations of the present disclosure may be assembler instructions, Instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer-readable program instructions 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 some embodiments, aspects of the disclosure are implemented by personalizing an electronic circuit, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA), with state information of computer-readable program instructions, which can execute the computer-readable program instructions.

Various aspects of the present disclosure are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.

These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.

The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The computer program product may be embodied in hardware, software or a combination thereof. In an alternative embodiment, the computer program product is embodied in a computer storage medium, and in another alternative embodiment, the computer program product is embodied in a Software product, such as a Software Development Kit (SDK), or the like.

The foregoing description of the various embodiments is intended to highlight various differences between the embodiments, and the same or similar parts may be referred to each other, and for brevity, will not be described again herein.

It will be understood by those skilled in the art that in the method of the present invention, the order of writing the steps does not imply a strict order of execution and any limitations on the implementation, and the specific order of execution of the steps should be determined by their function and possible inherent logic.

If the technical scheme of the application relates to personal information, a product applying the technical scheme of the application clearly informs personal information processing rules before processing the personal information, and obtains personal independent consent. If the technical scheme of the application relates to sensitive personal information, a product applying the technical scheme of the application obtains individual consent before processing the sensitive personal information, and simultaneously meets the requirement of 'express consent'. For example, at a personal information collection device such as a camera, a clear and significant identifier is set to inform that the personal information collection range is entered, the personal information is collected, and if the person voluntarily enters the collection range, the person is regarded as agreeing to collect the personal information; or on the device for processing the personal information, under the condition of informing the personal information processing rule by using obvious identification/information, obtaining personal authorization by modes of popping window information or asking a person to upload personal information of the person by himself, and the like; the personal information processing rule may include information such as a personal information processor, a personal information processing purpose, a processing method, and a type of personal information to be processed.

Having described embodiments of the present disclosure, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen in order to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A human-computer playing method, comprising:

obtaining first drop point data of current chess step drops of players playing in the current chess game;

acquiring second drop point data of the current chess step drop recommended by the intelligent chess player according to the chessboard data of the chess player before the current chess step drop;

and adjusting the chess force level of the intelligent chess player according to the first piece point data and the second piece point data, wherein the chess force level determines the third piece point data of the intelligent chess player for the next step.

2. The method of claim 1, further comprising:

before the current chess game is played, adjusting the initial chess force level of the intelligent chess player in the current chess game according to the chess game information of the last chess game of the playing player, wherein the chess game information comprises the playing process of the playing player recorded by the intelligent chess player in the interactive playing process.

3. The method of claim 2, wherein said adjusting a force level of said smart player based on said first and second drop point data comprises:

determining a difference between the moving method of the first falling point data and the moving method of the second falling point data according to the first falling point data and the second falling point data, wherein the difference represents a difference of levels of good and bad chess force levels between the moving methods;

and determining the chess force level of the intelligent chess player corresponding to the next chess step according to the difference and the initial chess force level.

4. The method of claim 2, wherein said adjusting said force level of said smart player in accordance with said first and second fall point data comprises:

and determining the chess force level of the intelligent chess hand corresponding to the step of the next chess according to the difference and the chess force level of the intelligent chess hand corresponding to the step of the previous chess.

5. The method of claim 1, wherein said obtaining second drop point data for a current step drop by an intelligent player as recommended by board data prior to the current step drop by the player comprises:

according to the chessboard data of the players before the current chess step is dropped, the intelligent chess players generate M recommended walking methods corresponding to the current chess step;

and sorting the M recommended approaches, and selecting the falling sub-point data corresponding to the K-th sorting approach as second falling sub-point data, wherein K is an integer which is greater than or equal to 1 and less than or equal to M.

6. The method of claim 1, wherein said obtaining second drop point data for a current step drop by an intelligent player as recommended by board data prior to the current step drop by the player comprises:

and the intelligent chess players recommend and generate second piece falling point data of the current chess step falling according to the historical optimal chess force level and the chessboard data before the current chess step falling of the players.

7. The method of claim 2, wherein said adjusting an initial force level of said intelligent players in a current game based on game information of a game on said playing player comprises:

determining the chess force level of the playing player according to the chess game information of the last chess game of the playing player;

and adjusting the initial chess force level of the intelligent chess players of the current chess game according to the chess force level of the playing players.

8. The method of claim 7, wherein the board game information includes all of the first drop point data for a board game on the playing player,

the determining the playing force level of the playing player according to the game information of the last game of the playing player comprises:

respectively determining second sub-point data recommended by the intelligent chess players corresponding to the first sub-point data of each chess step of the playing players;

determining the difference between the running method of the first sub-point data of each chess step of the players playing the chess and the corresponding running method of the second sub-point data recommended by the intelligent chess players;

and determining the chess force level of the playing player according to the difference of the first sub-point data of each chess step of the playing player.

9. The method of claim 2, wherein said adjusting an initial force level of said intelligent player in a current game based on game information of a last game of said player comprises:

under the condition that the game information of the last game is a playing balance, improving the initial playing force level of the intelligent players of the current game;

or, under the condition that the game information of the last game is the game of winning the game of the players, improving the initial game force level of the intelligent players in the current game;

or, in the case that the game information of the last game is that the playing player is lost, the initial game level of the intelligent player of the current game is lowered.

10. A human-computer playing device, comprising:

the first obtaining module is used for obtaining first piece-falling point data of the current piece falling of the current chess step of the player playing in the current chess game;

the second obtaining module is used for obtaining second falling point data of the current chess step falling piece recommended and generated by the intelligent chess player according to the chessboard data before the current chess step falling piece;

and the adjusting module is used for adjusting the chess force level of the intelligent chess player according to the first and second piece-dropping point data, and the chess force level determines the third piece-dropping point data of the intelligent chess player for dropping the next chess step.

11. An electronic device, comprising:

a processor;

a memory for storing processor-executable instructions;

wherein the processor is configured to invoke the memory-stored instructions to perform the method of any one of claims 1 to 9.

12. A computer readable storage medium having computer program instructions stored thereon, which when executed by a processor implement the method of any one of claims 1 to 9.