CN114742420A - Three-way chessboard chess-playing evaluation method and device, electronic equipment and storage medium - Google Patents
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Abstract
The invention provides a three-way chessboard chess-playing evaluation method, a device, electronic equipment and a storage medium, wherein AI records first process data generated in the process of man-machine interaction chess-playing and second process data after the chess-playing is finished, the first process data comprises position information of a first user falling point, the number of air of the user falling point and the number of air of the AI falling point after the user performs the falling, a chess type formed by the user falling point after the user performs the falling, and the like; the second process data comprises the user accumulated number of the food, the number of the illegal drop points falling into the preset user accumulated number of the AI drop points and the number of the user drop points. By integrating the data, the chess skill level of the user can be evaluated from multiple dimensions such as weiqi rule cognition, chess skill, computing power, big game, logical thinking and the like, so that the learning interest of the user is better excited, and the playing level of the user is improved.
Description
Technical Field
The application relates to the technical field of go evaluation, in particular to a three-way chessboard chess evaluation method, a three-way chessboard chess evaluation device, electronic equipment and a storage medium.
Background
At present, there are many products for eating subclasses of weiqi on the market, the chessboard mainly comprises 9 paths and 13 paths, but the playing rules of the existing 9 paths and 13 paths of chessboard are more, which is difficult to be mastered by beginners, especially low-age beginners, and even can generate strong frustration, therefore, in order to reduce the learning threshold of weiqi and improve the interest of the beginners, three paths of chessboard are developed on the market in recent years, the playing rules of the three paths of chessboard are simple, and the weiqi chessboard is suitable for the low-age beginners to learn. However, at present, there is no chess evaluation product after the man-machine playing process, which is specially set for the three-way chessboard playing rules, and improvement is urgently needed.
Disclosure of Invention
In view of the above, the present application provides a three-way chessboard chess evaluation method, device, electronic device and storage medium to solve the above technical problems.
The disclosed exemplary embodiment provides a three-way chessboard chess-playing evaluation method, which comprises the following steps:
obtaining chessboard data information, wherein the chessboard data information is used for representing any point location included in the three chessboards as one of a null point location, an AI drop point and a user drop point;
carrying out human-computer interaction chess playing with a user based on the chessboard data information and a preset human-computer chess playing strategy, and recording first process data generated in the human-computer interaction chess playing process and second process data after the chess playing is finished;
generating evaluation data according to the first process data and the second process data and feeding back the evaluation data to the user;
the first process data comprises position information of a first user falling point, the gas number of the user falling point and the gas number of an AI falling point after the user performs falling, and a chess pattern formed by the user falling point after the user performs falling;
the second process data comprises the user accumulated number of the food, the number of illegal drop points falling into the preset user accumulated number of the drop points, the number of AI drop points and the number of user drop points.
In some exemplary embodiments, the chess types include a true eye chess type, a long chess type, a play two and a play one chess type.
In some exemplary embodiments, the illegal child point includes a point of forbidding and a point of hijacking.
In some exemplary embodiments, the human-machine playing policy includes a first preset policy; the first preset strategy is as follows:
in response to determining that there is an empty location at the center of the three-way board, performing a drop action at the empty location at the center of the three-way board;
in response to determining that there is no empty point located at the center of the three chessboard paths and there is an empty point located at the three chessboard paths, performing a drop action at the empty point located at the three chessboard paths;
and in response to determining that no empty point positions at the center and the edge of the three-way chessboard exist and that empty point positions at the corners of the three-way chessboard exist, executing a drop action at the empty point positions at the corners of the three-way chessboard.
In some exemplary embodiments, the human-machine playing strategies further include a second preset strategy that is executed in preference to the first preset strategy; the second preset strategy is as follows:
for any of the empty point locations, in response to determining that the empty point location and the AI drop point encompass a user drop point, performing a drop action at the empty point location.
In some exemplary embodiments, the human-machine playing strategy further comprises:
determining the illegal child falling points in all the empty point positions according to a preset playing rule;
and executing a first preset strategy or a second preset strategy on the empty point positions except the illegal drop points.
In some exemplary embodiments, the conditions for the end of a game are: the number of AI falling points in the chessboard data information is 0 or the number of user falling points is 0;
and the party with the number of 0 can not execute the first preset strategy or the second preset strategy at the empty points except the illegal drop points.
Based on the same inventive concept, the exemplary embodiment of the present disclosure further provides a three-way chessboard playing device, comprising:
the acquisition module is configured to acquire chessboard data information, wherein the chessboard data information is used for representing any point location included in the three chessboards as one of a null point location, an AI drop point and a user drop point;
the computing module is configured to carry out human-computer interaction chess playing with a user based on the chessboard data information and a preset human-computer chess playing strategy, and record first process data generated in the human-computer interaction chess playing process and second process data after the chess playing is finished;
the evaluation module is configured to generate evaluation data according to the first process data and the second process data and feed the evaluation data back to the user;
the first process data comprises position information of a first user falling point, the gas number of the user falling point and the gas number of an AI falling point after the user performs falling, and a chess pattern formed by the user falling point after the user performs falling;
the second process data comprises the user accumulated number of the food, the number of illegal drop points falling into the preset user accumulated number of the drop points, the number of AI drop points and the number of user drop points.
Based on the same inventive concept, the exemplary embodiments of the present disclosure also provide an electronic device, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the method as described in any one of the above items when executing the program.
Based on the same inventive concept, the exemplary embodiments of the present disclosure also provide a non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the method as described in any one of the above.
From the foregoing, it can be seen that the present application provides a three-way board game evaluation method, apparatus, electronic device and storage medium, the method collects, calculates and stores first process data during the playing process and second process data after the playing process is finished, generates evaluation data based on the first process data and the second process data, the evaluation data can be displayed from at least five dimensions of Weiqi rule cognition, draughty skill, computing power, general view, logic thinking and the like in the form of radar maps and the like for the reference of users, the five evaluation dimensions comprehensively cover the capacity required by the go playing, the evaluation result can reasonably reflect the real chess skill level of the user, the user can carry out special promotion training in a targeted manner, and the learning trouble of the user caused by only taking win-or-loss as an evaluation result in the prior art is effectively avoided.
Drawings
In order to more clearly illustrate the technical solutions in the present application or related technologies, the drawings required for the embodiments or related technologies in the following description are briefly introduced, and it is obvious that the drawings in the following description are only the embodiments of the present application, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a schematic diagram of an application scenario of an exemplary embodiment of the present disclosure;
FIG. 2 is a schematic flow diagram of a three-way board game evaluation method according to an exemplary embodiment of the present disclosure;
FIG. 3 is a schematic diagram of each intersection of a three-way chessboard according to an exemplary embodiment of the present disclosure;
FIG. 4 is an exemplary schematic diagram of evaluation data embodied in the form of a radar map in accordance with an exemplary embodiment of the present disclosure;
FIG. 5 is a schematic flow diagram of a three-way board playing strategy according to an exemplary embodiment of the present disclosure;
fig. 6 is a schematic structural diagram of a three-way board play evaluation device according to an exemplary embodiment of the present disclosure;
fig. 7 is a schematic structural diagram of an electronic device according to an exemplary embodiment of the present disclosure.
Detailed Description
The principles and spirit of the present application will be described below with reference to a number of exemplary embodiments. It should be understood that these embodiments are presented only to enable those skilled in the art to better understand and to implement the present disclosure, and are not intended to limit the scope of the present application in any way. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
According to the embodiment of the disclosure, a training method of a text processing model, a text processing method and related equipment are provided.
In this document, it is to be understood that any number of elements in the figures are provided by way of illustration and not limitation, and any nomenclature is used for differentiation only and not in any limiting sense.
For convenience of understanding, terms referred to in the embodiments of the present disclosure are explained below:
neural Networks (ANNs): a practical artificial neural network model is built according to the principle of the biological neural network and the requirement of practical application, a corresponding learning algorithm is designed, certain intelligent activity of the human brain is simulated, and then the practical artificial neural network model is technically realized to solve the practical problem.
Policy network model (PolicyNet, policy network): after the neural network is used for training (learning), the next step of screening and determining the falling point can be carried out based on the current chessboard.
Estimation network model (ValueNet, estimation network): the method is a neural network model which is obtained by training (learning) a neural network through a large number of samples and can predict the win ratio value of various types of fellows in the next step based on the current state of a chessboard. The neural network has the characteristics of a neural network and has certain self-learning capability.
Fast walk subnetwork model (FastNet, fast walk subnetwork): the neural network model is obtained by training (learning) the neural network and can perform subsequent rapid alternate falling based on the current state of the chessboard.
User drop point: the position of the user during the playing process of the human computer.
AI falling point: the position of the machine side during the playing process of the human machine.
Lifting: in the go, the chess pieces of the opposite side are taken out (lifted) of the chessboard to be lifted, and the air-free state means that no empty intersection points exist around the chess pieces of the same side connected with the chess pieces, namely, no air exists;
a forbidden point: if one party drops at a certain intersection on the chessboard, the chessman is in an airless state and the son of the other party (a dipper) cannot be lifted away, and the intersection to be dropped on the chessboard is called a forbidden point of the party;
beating and eating: after the finger of one party falls, only one breath is left for one or some children of the other party, and if the breath is not kept, the next hand is lifted;
hijacking and hijacking point: one player of the other party can be lifted after the player falls, but only one piece of the chess piece fallen by the player is left, namely the player of the other party can reversely lift the player of the other party after the player falls, so that the two parties can repeatedly lift the player, and the player is hijacked, wherein the player point of the other party is a hijack point, the hijack point needs to be cached, and the other party cannot fall immediately.
True eye chess type: one or two empty intersections are surrounded by the chess pieces connected together, and the chess type is a true-eye chess type.
Long qi chess type: the chess pieces are placed next to the chess pieces of the player, and the gas of the chess pieces of the player can be increased, namely the chess is of a long gas chess type.
Two-to-one chess type: one party can lift two pieces of the other party, and the other party can immediately lift one piece, so the chess is of a two-to-one chess type.
The principles and spirit of the present application are explained in detail below with reference to several representative embodiments of the present disclosure.
Summary of The Invention
The evaluation (or game evaluation) related to the weiqi refers to that after the game is finished each time, the AI side presents a related evaluation result for the performance of the user in the game, and simply informs the user of losing and winning under the condition of meeting the game finishing condition after the current three-way chessboard is played by man-machine, but for a beginner, the probability that the result of the man-machine game is lost is higher, and the probability that the result is lost after the training for a period of time is higher, so that the user can hardly see the progress of the user from the result and can hardly see the aspects from which the user needs to improve from the result of losing and winning, and the learning trouble of the user is caused. Therefore, a set of multi-dimensional evaluation system is needed to reflect the real chess skill level of the user, and the evaluation system needs to show the advantages and disadvantages of the user except for win-loss, so that the user can perform special promotion training aiming at the related disadvantages.
In view of the problems in the prior art, the present disclosure provides a three-way chessboard chess-playing evaluation method, device, electronic device and storage medium, wherein in the method, an AI records first process data generated in a man-machine interaction game-playing process and second process data after game-playing is finished, the first process data includes position information of a first user landing point, the number of air of the user landing point and the number of air of the AI landing point after the user performs landing, a chess type formed by the user landing point after the user performs landing, and the like; the second process data comprises the user accumulated number of the food, the number of the illegal drop points falling into the preset user accumulated number of the AI drop points and the number of the user drop points. By combining the first process data and the second process data, the real chess skill level of the user can be evaluated from multiple dimensions such as go rule cognition, chess skill, computing power, big game view, logical thinking and the like, the evaluation result is more real and effective, the learning interest of the user is better excited, and the playing level of the user is improved.
In addition, with the improvement of the evaluation function of the three chessboard paths, the evaluation device can also be used for the pre-class evaluation of the Weiqi of the user, thereby enhancing the interest and the participation sense and enhancing the basic understanding of the Weiqi of the user.
Having described the general principles of the present disclosure, various non-limiting embodiments of the present disclosure are described in detail below.
Application scene overview
Referring to fig. 1, it is a schematic diagram of an application scenario of the three-way chessboard chess evaluation method provided by the embodiment of the present disclosure. The application scenario includes a terminal device 101, a server 102, and a data storage system 103. The terminal device 101, the server 102, and the data storage system 103 may be connected through a wired or wireless communication network. The terminal device 101 includes, but is not limited to, a desktop computer, a mobile phone, a mobile computer, a tablet computer, a media player, a smart wearable device, a Personal Digital Assistant (PDA), or other electronic devices capable of implementing the above functions. The server 102 and the data storage system 103 may be independent physical servers, may also be a server cluster or distributed system formed by a plurality of physical servers, and may also be cloud servers providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDNs, and big data and artificial intelligence platforms.
The server 102 is used for providing three-way board game playing learning service for the user of the terminal device 101, and a client communicated with the server 102 is installed in the terminal device 101.
The server 102 transmits the chessboard data information after the chess playing through the human-computer interaction to the client of the terminal device 101 through the communication network, and displays the chessboard data information on the display interface corresponding to the client. Meanwhile, the server 102 determines, based on the chessboard data information, first process data generated in the chess playing process and second process data after the chess playing is finished to generate evaluation data, sends the evaluation data to the client of the terminal device 101, and displays the evaluation data on a display interface corresponding to the client, so that the display process of an evaluation result is realized, and then three chessboard chess playing evaluation tasks of the user are completed.
The three-way board game evaluation method, device, electronic device and storage medium according to the exemplary embodiments of the present disclosure are described below with reference to the application scenario of fig. 1. It should be noted that the above application scenarios are merely illustrated for the convenience of understanding the spirit and principles of the present disclosure, and the embodiments of the present disclosure are not limited in this respect. Rather, embodiments of the present disclosure may be applied to any scenario where applicable.
Exemplary method
Some embodiments of the present application provide a three-way chessboard playing evaluation method, as shown in fig. 2, including:
s201, chessboard data information is obtained, and the chessboard data information is used for representing any point location included in the three chessboard paths as one of a null point location, an AI drop point and a user drop point;
s202, performing man-machine interaction chess playing with a user based on the chessboard data information and a preset man-machine chess playing strategy, and recording first process data generated in the man-machine interaction chess playing process and second process data after the chess playing is finished;
s203, generating evaluation data according to the first process data and the second process data and feeding back the evaluation data to the user;
the first process data comprises position information of a first user falling point, the gas number of the user falling point and the gas number of an AI falling point after the user performs falling, and a chess pattern formed by the user falling point after the user performs falling;
the second process data comprises the user accumulated number of the food, the number of illegal drop points falling into the preset user accumulated number of the drop points, the number of AI drop points and the number of user drop points.
The evaluation method according to the embodiment of the present application is performed after the completion of the human-computer interaction game, but the data used for the evaluation is derived from first process data during the human-computer interaction game and second process data after the completion of the human-computer interaction game, the first and second process data being generated based on the state of the intersection (the state of the intersection includes a null point, an AI drop point, and a user drop point) in the board data information. Specifically, after the user performs the falling action, chessboard data information after the user currently steps is obtained, and the chessboard data information is used for recording the state of each intersection on the whole chessboard and the distribution state of all the chessmen based on the intersection state after the user currently steps. The state of each intersection may use the following data structure: the coordinate and the intersection state may specifically represent the intersection state on the whole chessboard after the current step of the user through the data structure of the coordinate and the intersection state, so as to obtain the chessboard data information. The three-way chessboard is a 3 x 3 go chessboard, and there are 9 cross points (i.e. touchable sub points), so the layout data of the chessboard can correspondingly include 9 data sets of the [ coordinate, cross point state ] structure data. For example, referring to fig. 3, a cartesian coordinate system is constructed with the C2 position as the origin of coordinates for the go board, i.e. any position on the go board can be represented. The intersection state in the data structure is represented by the number "1" as a user's chess piece (black chess), the number "0" as no chess piece, the number "-1" as an AI chess piece (white chess), and the intersection state at any position on the go board can be represented by the above-mentioned number. Based on the above data structure, in the chessboard shown in FIG. 3, the black chess in the A position can be represented as [ (2,2),1], the white chess in the B1 position can be represented as [ (0,1), -1], and the empty point in the C3 position can be represented as [ (3,3),0 ].
After the user and the AI execute each step of falling chess, the AI confirms and stores state data of each intersection in the chessboard, and further obtains the distribution state of all the chess pieces according to the states of the intersections, wherein the distribution state can be used for judging the chess type (the chess type can be a true eye chess type, a long-air chess type, a two-to-one chess type and the like) and illegal falling chess points (access points, hijack points and the like) of the current chessboard, so that the position information of the falling chess piece of the user, the position information of the falling chess piece position of the AI, the number of eaten users after the user executes the falling chess piece, the number of eaten users after the AI executes the falling chess piece, the number of the fallen chess pieces of the users after the AI execution of the falling chess piece, the number of the fallen chess pieces of the users after the user executes the falling piece and the number of the fallen chess pieces of the AI falling piece points, the number of the fallen chess pieces of the users after the user executes the falling piece into the illegal falling piece points (0 or 1), and the chess type formed by the fallen chess pieces of the users after the user executes the falling piece; and the position information of the first user's falling point, the air number of the user's falling point and the air number of the AI falling point after the user performs the falling, and the chess pattern formed by the user's falling point after the user performs the falling can be used as the first process data in the playing process.
And after the chess playing is finished, obtaining the number of AI drop points and the number of user drop points in the data information of the chessboard after the chess playing is finished, and respectively accumulating the number of users eating the drop points after the user executes the drop, the number of users eating the drop after the AI execution, and the number of illegal drop points which the user drop points fall into after the user executes the drop in the chess playing process to obtain the accumulated number of users eating the drop, and the accumulated number of the illegal drop points which the user drop points fall into preset. And taking the number of the AI drop points, the number of the user drop points, the cumulative number of the users, and the cumulative number of the users falling into the preset illegal drop points as second process data after the chess playing is finished.
The first process data and the second process data are classified according to preset rules, and therefore the chess skill level of a user can be comprehensively evaluated from five dimensions of weiqi rule cognition, piece eating skill, computing power, big chess views, logical thinking and the like, the evaluation process is exemplified as shown in the following table, wherein the upper limit of each dimension score in the table is 4, and the lower limit of each dimension score in the table is 0.
TABLE 1
After performing the relevant evaluation policy according to the above table, the user exemplary score may be: the game of the game is divided into 4 parts of weiqi rule cognition, 3 parts of draughting skills, 3 parts of calculation power, 4 parts of large local view and 2 parts of logical thinking, the radar map displayed to the user is shown in figure 4, and the user can clearly know that the self weiqi rule cognition and the large local view are dominant through the radar map, but the game playing level still needs to be improved from three dimensions of the logical thinking, the draughting skills and the calculation power.
The three-way chessboard chess evaluation method comprises the steps of collecting, calculating and storing first process data in the chess playing process and second process data after chess playing is finished, generating evaluation data based on the first process data and the second process data, and displaying the evaluation data in the form of radar maps from at least five dimensions of weiqi rule cognition, draughting skills, computing power, big game, logical thinking and the like so as to be referred by users.
In some embodiments, in order to obtain accurate and effective first process data and second process data, the AI side needs to execute a human-machine playing strategy for the three-way chessboard, the human-machine playing strategy comprising a first preset strategy; the first preset strategy is as follows:
in response to determining that there is an empty location at the center of the three-way board, performing a drop action at the empty location at the center of the three-way board;
in response to determining that there is no empty point located at the center of the three chessboard paths and there is an empty point located at the three chessboard paths, performing a drop action at the empty point located at the three chessboard paths;
and in response to determining that no empty point positions at the center and the edge of the three-way chessboard exist and that empty point positions at the corners of the three-way chessboard exist, executing a drop action at the empty point positions at the corners of the three-way chessboard.
It should be noted that the human-machine playing strategy of the three-way chessboard of the present embodiment is different from the human-machine playing strategy of the 9-way and 13-way chessboard, for example, the rule of "golden-angle silvery grass belly" in the 9-way and 13-way chessboard does not exist in the three-way chessboard, based on the particularity of the three-way chessboard, when the AI side performs the falling motion by policyenet, vaenluet and FastNet, the playing strategy of "golden-angle silvery grass corner" is followed, as shown in fig. 3, a represents the center position by a, B1, B2, B3 and B4 represent 4 side positions respectively, and C1, C2, C3 and C4 represent 4 corner positions respectively, the falling sub-priorities of the three types of positions represented by ABC in the three-way chessboard are a (center position) > B (side position) > C (corner position), that if the a position can be lower, the B position is preferred, otherwise, the C position cannot be lower. The first preset strategy is simpler than the strategy for playing on a 9-way or 13-way chessboard, for example, the 9-way chessboard has 4 "gold corners" (i.e. angular positions) and 28 "silver sides" (i.e. edge positions) and the 3-way chessboard has only 1 "belly" (i.e. central position) and 4 "silver sides" (i.e. edge positions), in other words, the 9-way chessboard has 4 better positions for the first-hand chess to select, and the three-way chessboard has only one better position for the first-hand chess to select.
In some embodiments, the human-machine playing strategies further comprise a second preset strategy executed in preference to the first preset strategy; the second preset strategy is as follows:
for any of the empty point locations, in response to determining that the empty point location and the AI drop point encompass a user drop point, performing a drop action at the empty point location.
Specifically, when the falling point and the existing AI falling point can surround the user falling point, that is, the falling point can eat chess, the falling point can fall directly without considering the first preset strategy no matter whether the falling point is at the center position, the side position or the corner position.
In some embodiments, the human-machine playing strategies further include a third preset strategy, which includes:
determining the illegal child falling points in all the empty point positions according to a preset playing rule;
and executing a first preset strategy or a second preset strategy at the empty point positions (namely legal falling points) except the illegal falling points.
The illegal child points comprise access control points and hijack points. That is, the AI party needs to execute the first preset strategy or the second preset strategy at the empty point position except the entry barring point and the hijack point, that is, the third preset strategy takes precedence over the first preset strategy and the second preset strategy.
In some embodiments, the determination process of the AI side in executing the human-machine playing strategy is as shown in fig. 5, specifically as follows:
a, circularly traversing each empty point in the current chessboard;
b, judging whether a null point is an illegal drop point or not; in response to the empty point location being an illegal drop point (a point of forbidding or a point of hijacking), skipping the empty point location; responding to the fact that the empty point position is a legal falling point, and continuously judging whether the falling can be carried out or not after the empty point position is subjected to falling;
c, responding to the fact that the person can eat after the person falls at the empty point position, and then performing the person falls; in response to the fact that the person cannot eat after the person falls at the empty point, whether the person can be beaten after the person falls at the empty point is continuously judged;
d, in response to being stuttered after the falling is performed at the empty point, skipping the empty point; in response to the fact that the empty point can not be stuttered after the empty point executes the drop, whether the empty point is the empty point with the highest priority in the first preset strategy or not is continuously judged;
e, responding to the empty point bit with the highest priority in the first preset strategy, executing falling; and in response to the null point bit not being the null point bit with the highest priority in the first preset strategy, skipping the null point bit.
The step b is a third preset strategy, the step c is a second preset strategy, and the step d is a first preset strategy, so that the execution sequence of the three preset strategies is the third preset strategy, the second preset strategy and the first preset strategy.
Step d is a process of judging whether the empty point position is the highest priority in a first preset strategy, the highest priority is a relative concept, and if the center position can fall, the center position is the falling point with the highest priority; if the center position can not fall, and the edge position can fall, the edge position is the falling point with the highest priority; if the center position and the edge position can not fall, and the corner position can fall, the corner position is the falling point with the highest priority.
In some embodiments, the conditions for the end of a game are: the number of AI child points in the chessboard data information is 0 or the number of user child points is 0, and one party with the number of 0 cannot execute a first preset strategy or a second preset strategy at the empty point position outside the illegal child points.
The three-way chessboard in this embodiment also has different playing end conditions from the 9-way or 13-way chessboard, and the playing end conditions of the 9-way or 13-way chessboard are as follows: if the other party can not fall after the one party falls, or the other party can be immediately raised after the fall, the game is finished. The playing end conditions of the three chessboard paths in the embodiment are as follows: one side chess pieces are completely eaten and cannot fall, namely the AI side needs to judge whether the number of AI falling points and the number of user falling points in the chessboard data information are 0 or not, and the side with 0 is completely eaten; and under the condition that one party is 0, the AI party also needs to judge that the party which is 0 can not execute the first preset strategy or the second preset strategy at the empty point position except the illegal falling point, namely, the party which is 0 can not execute the effective falling again, namely, the playing is finished. The playing end conditions of the three-way chessboard are simpler than those of 9-way or 13-way chessboard and the like, and the three-way chessboard is convenient for beginners to quickly master.
Based on the same inventive concept, corresponding to the method of any embodiment, the application also provides a three-way chessboard playing device.
Referring to fig. 6, a three-way board playing device includes:
an obtaining module 601, configured to obtain chessboard data information, where the chessboard data information is used to represent any point location included in the three chessboards as one of a null point location, an AI drop point, and a user drop point;
the computing module 602 is configured to perform human-computer interaction chess playing with a user based on the chessboard data information and a preset human-computer chess playing strategy, and record first process data generated in the human-computer interaction chess playing process and second process data after the chess playing is finished;
an evaluation module 603 configured to generate evaluation data from the first and second process data and to feed back to the user;
the first process data comprises position information of a first user falling point, the gas number of the user falling point and the gas number of an AI falling point after the user performs falling, and a chess pattern formed by the user falling point after the user performs falling;
the second process data comprises the user accumulated amount of food, the amount of illegal food falling points which are accumulated by the user and fall into the preset illegal food falling points, the amount of AI food falling points and the amount of user food falling points.
In some optional embodiments, the chess types include a true eye chess type, a long chess type, and a play-two-in-one chess type.
In some optional embodiments, the illegal child point includes a point of forbidding and a point of hijacking.
In some optional embodiments, the human-machine playing strategy comprises a first preset strategy; the first preset strategy is as follows:
in response to determining that there is an empty location at the center of the three-way board, performing a drop action at the empty location at the center of the three-way board;
in response to determining that there is no empty point located at the center of the three chessboard paths and there is an empty point located at the three chessboard paths, performing a drop action at the empty point located at the three chessboard paths;
and in response to determining that no empty point positions at the center and the edge of the three-way chessboard exist and that empty point positions at the corners of the three-way chessboard exist, executing a drop action at the empty point positions at the corners of the three-way chessboard.
In some optional embodiments, the human-machine playing strategy further comprises a second preset strategy executed in preference to the first preset strategy; the second preset strategy is as follows:
for any of the empty point locations, in response to determining that the empty point location and the AI drop point encompass a user drop point, performing a drop action at the empty point location.
In some optional embodiments, the human-machine playing strategy further comprises:
determining the illegal child falling points in all the blank point positions according to a preset game rule;
and executing a first preset strategy or a second preset strategy on the empty point positions except the illegal drop points. In some optional embodiments, the conditions for the end of playing are: the number of AI drop points in the chessboard data information is 0 or the number of user drop points is 0, and one party with the number of 0 cannot execute the first preset strategy or the second preset strategy at empty points other than the illegal drop points.
Based on the same inventive concept, corresponding to the method of any of the above embodiments, the present application further provides an electronic device, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the three-way chessboard chess-playing evaluation method of any of the above embodiments when executing the program.
Fig. 7 is a schematic diagram illustrating a more specific hardware structure of an electronic device according to this embodiment, where the device may include: a processor 1010, a memory 1020, an input/output interface 1030, a communication interface 1040, and a bus 1050. Characterized in that processor 1010, memory 1020, input/output interface 1030, and communication interface 1040 are communicatively coupled to each other within the device via bus 1050.
The processor 1010 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an Application Specific Integrated Circuit (ASIC), or one or more Integrated circuits, and is configured to execute related programs to implement the technical solutions provided in the embodiments of the present disclosure.
The Memory 1020 may be implemented in the form of a ROM (Read Only Memory), a RAM (Random Access Memory), a static storage device, a dynamic storage device, or the like. The memory 1020 may store an operating system and other application programs, and when the technical solution provided by the embodiments of the present specification is implemented by software or firmware, the relevant program codes are stored in the memory 1020 and called to be executed by the processor 1010.
The input/output interface 1030 is used for connecting an input/output module to input and output information. The i/o module may be configured as a component within the device (not shown) or may be external to the device to provide corresponding functionality. It is characterized in that the input device can include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output device can include a display, a loudspeaker, a vibrator, an indicator light, etc.
The communication interface 1040 is used for connecting a communication module (not shown in the drawings) to implement communication interaction between the present apparatus and other apparatuses. The communication module is characterized in that the communication module can realize communication in a wired mode (such as USB, network cable and the like) and also can realize communication in a wireless mode (such as mobile network, WIFI, Bluetooth and the like).
It should be noted that although the above-mentioned device only shows the processor 1010, the memory 1020, the input/output interface 1030, the communication interface 1040 and the bus 1050, in a specific implementation, the device may also include other components necessary for normal operation. In addition, those skilled in the art will appreciate that the above-described apparatus may also include only those components necessary to implement the embodiments of the present description, and not necessarily all of the components shown in the figures.
The electronic device of the foregoing embodiment is used to implement the corresponding three-way board playing evaluation method in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiments, which are not described herein again.
Exemplary program product
Based on the same inventive concept, corresponding to any of the above-described embodiment methods, the present disclosure also provides a non-transitory computer-readable storage medium storing computer instructions for causing the computer to perform the three-way board game evaluation method according to any of the above-described embodiments.
The non-transitory computer readable storage medium may be any available medium or data storage device that can be accessed by a computer, including but not limited to magnetic memory (e.g., floppy disks, hard disks, magnetic tape, magneto-optical disks (MOs), etc.), optical memory (e.g., CDs, DVDs, BDs, HVDs, etc.), and semiconductor memory (e.g., ROMs, EPROMs, EEPROMs, non-volatile memory (NAND FLASH), Solid State Disks (SSDs)), etc.
The storage medium of the above embodiment stores computer instructions for causing the computer to execute the three-way chess board evaluation method according to any one of the above exemplary method embodiments, and has the beneficial effects of the corresponding method embodiments, which are not described herein again.
As will be appreciated by one skilled in the art, embodiments of the present invention may be embodied as a system, method or computer program product. Accordingly, the present disclosure may be embodied in the form of: entirely hardware, entirely software (including firmware, resident software, micro-code, etc.) or a combination of hardware and software, and is referred to herein generally as a "circuit," module "or" system. Furthermore, in some embodiments, the invention may also be embodied in the form of a computer program product in one or more computer-readable media having computer-readable program code embodied in the medium.
Any combination of one or more computer-readable media may be employed. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive example) of the computer readable storage medium may include, for example: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
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 program instructions. These computer 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 program instructions may also be stored in a computer-readable medium that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable medium produce an article of manufacture including instruction means which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer 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 or other programmable apparatus provide processes for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.
Moreover, while the operations of the method of the invention are depicted in the drawings in a particular order, this does not require or imply that the operations must be performed in this particular order, or that all of the illustrated operations must be performed, to achieve desirable results. Rather, the steps depicted in the flowcharts may change the order of execution. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step execution, and/or one step broken down into multiple step executions.
Use of the verbs "comprise", "comprise" and their conjugations in this application does not exclude the presence of elements or steps other than those stated in this application. The article "a" or "an" preceding an element does not exclude the presence of a plurality of such elements.
While the spirit and principles of the invention have been described with reference to several particular embodiments, it is to be understood that the invention is not limited to the disclosed embodiments, nor is the division of aspects, which is for convenience only as the features in such aspects may not be combined to benefit. The invention is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims. The scope of the following claims is to be accorded the broadest interpretation so as to encompass all such modifications and equivalent structures and functions.
Those of ordinary skill in the art will understand that: the discussion of any embodiment above is meant to be exemplary only, and is not intended to intimate that the scope of the disclosure, including the claims, is limited to these examples; within the context of the present application, features from the above embodiments or from different embodiments may also be combined, steps may be implemented in any order, and there are many other variations of the different aspects of the embodiments of the present application as described above, which are not provided in detail for the sake of brevity.
In addition, well-known power/ground connections to Integrated Circuit (IC) chips and other components may or may not be shown in the provided figures for simplicity of illustration and discussion, and so as not to obscure the embodiments of the application. Furthermore, devices may be shown in block diagram form in order to avoid obscuring embodiments of the application, and this also takes into account the fact that specifics with respect to implementation of such block diagram devices are highly dependent upon the platform within which the embodiments of the application are to be implemented (i.e., specifics should be well within purview of one skilled in the art). Where specific details (e.g., circuits) are set forth in order to describe example embodiments of the application, it should be apparent to one skilled in the art that the embodiments of the application can be practiced without, or with variation of, these specific details. Accordingly, the description is to be regarded as illustrative instead of restrictive.
While the present application has been described in conjunction with specific embodiments thereof, many alternatives, modifications, and variations of these embodiments will be apparent to those of ordinary skill in the art in light of the foregoing description. For example, other memory architectures (e.g., dynamic ram (dram)) may use the embodiments discussed.
The present embodiments are intended to embrace all such alternatives, modifications and variances which fall within the broad scope of the appended claims. Therefore, any omissions, modifications, substitutions, improvements, and the like that may be made without departing from the spirit and principles of the embodiments of the present application are intended to be included within the scope of the present application.
Claims (10)
1. A three-way chessboard playing evaluation method is characterized by comprising the following steps:
obtaining chessboard data information, wherein the chessboard data information is used for representing any point location included in the three chessboards as one of a null point location, an AI drop point and a user drop point;
carrying out human-computer interaction chess playing with a user based on the chessboard data information and a preset human-computer chess playing strategy, and recording first process data generated in the human-computer interaction chess playing process and second process data after the chess playing is finished;
generating evaluation data according to the first process data and the second process data and feeding back the evaluation data to the user;
the first process data comprises position information of a first user falling point, the gas number of the user falling point and the gas number of an AI falling point after the user performs falling, and a chess pattern formed by the user falling point after the user performs falling;
the second process data comprises the user accumulated number of the food, the number of illegal drop points falling into the preset user accumulated number of the drop points, the number of AI drop points and the number of user drop points.
2. The evaluation method according to claim 1, wherein said chess types include a true eye chess type, a long chess type, and a play-by-play chess type.
3. The evaluation method according to claim 1, wherein the illegal child point comprises a point of entry prohibition and a point of hijacking.
4. The evaluation method according to claim 1, wherein the human-machine playing policy includes a first preset policy; the first preset strategy is as follows:
in response to determining that there is a null point located at the center of the three ways board, performing a drop action at the null point located at the center of the three ways board;
in response to determining that there is no empty point located in the center of the three ways of chessboard and there is an empty point located in the three ways of chessboard route, performing a doffing action on the empty point located in the three ways of chessboard route;
and in response to determining that no empty point positions at the center and the edge of the three-way chessboard exist and that empty point positions at the corners of the three-way chessboard exist, executing a drop action at the empty point positions at the corners of the three-way chessboard.
5. The evaluation method according to claim 1, wherein the human-machine playing policy further includes a second preset policy that is executed in preference to the first preset policy; the second preset strategy is as follows:
for any of the empty point locations, in response to determining that the empty point location and the AI drop point encompass a user drop point, performing a drop action at the empty point location.
6. The evaluation method according to claim 1, wherein the human-machine playing strategy further comprises:
determining the illegal child falling points in all the blank point positions according to a preset game rule;
and executing a first preset strategy or a second preset strategy on the empty point positions except the illegal drop points.
7. The evaluation method according to claim 1, wherein the conditions for the end of a play are: the number of AI falling points in the chessboard data information is 0 or the number of user falling points is 0;
and the party with the number of 0 can not execute the first preset strategy or the second preset strategy at the empty points except the illegal drop points.
8. A three-way chessboard playing device is characterized by comprising:
the acquisition module is configured to acquire chessboard data information, wherein the chessboard data information is used for representing any point location included in the three chessboards as one of a null point location, an AI (artificial intelligence) drop point and a user drop point;
the computing module is configured to carry out human-computer interaction chess playing with a user based on the chessboard data information and a preset human-computer chess playing strategy, and record first process data generated in the human-computer interaction chess playing process and second process data after the chess playing is finished;
the evaluation module is configured to generate evaluation data according to the first process data and the second process data and feed the evaluation data back to the user;
the first process data comprises position information of a first user falling point, the gas number of the user falling point and the gas number of an AI falling point after the user performs falling, and a chess pattern formed by the user falling point after the user performs falling;
the second process data comprises the user accumulated amount of food, the amount of illegal food falling points which are accumulated by the user and fall into the preset illegal food falling points, the amount of AI food falling points and the amount of user food falling points.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method according to any of claims 1 to 7 when executing the program.
10. A non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the method of any one of claims 1 to 7.
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