CN113935618B - Evaluation method and device for chess playing capability, electronic equipment and storage medium - Google Patents
Evaluation method and device for chess playing capability, electronic equipment and storage medium Download PDFInfo
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Abstract
The disclosure provides a chess playing capability evaluation method and device, electronic equipment and a storage medium. The method comprises the following steps: pushing the chess board to a display interface for displaying; searching and determining N recommended falling points according to a preset search breadth based on the current chess board, and sequencing the N recommended falling points to obtain a sequencing result, wherein N is more than or equal to 1; receiving the drop information of the target user, and grading the drop information based on the sequencing result to obtain a grading result; searching the chess board according to a preset searching breadth based on the fallen target user, determining a target falling point with the minimum deviation with a set winning rate value, and carrying out the next falling of the fallen target user according to the target falling point; and responding to the determination of meeting the playing ending condition, acquiring at least one scoring result corresponding to at least one piece of drop information of the target user, and performing evaluation on the target user according to the at least one scoring result to obtain an evaluation result. The chess force evaluation can be rapidly and accurately carried out.
Description
Technical Field
The present disclosure relates to the field of artificial intelligence technologies, and in particular, to a method and an apparatus for evaluating chess playing ability, an electronic device, and a storage medium.
Background
For some chess playing training institutions (especially go training institutions), the initial level of each student is different, and if the students simply rely on their own comments, the comments may not be accurate. Therefore, the chess playing ability of students needs to be evaluated.
However, the existing chess playing ability evaluation mode adopts a question making mode to test. The questions include basic knowledge, death and activity questions, etc. This method has the advantage of being easy to implement, but also has a number of disadvantages. The most prominent is that the evaluation result has large variance because the student strength cannot be comprehensively and systematically evaluated by the subject.
In another method, a student plays a game on multiple discs with AI (Artificial Intelligence) of different grades, and the student is finally rated with the result of playing the game. The method focuses more on actual combat than problem making, improves accuracy, but has the defect of long time consumption.
Disclosure of Invention
In view of the above, the present disclosure provides a method, an apparatus, an electronic device and a storage medium for evaluating chess playing ability, so as to solve or partially solve the above technical problems.
Based on the above purpose, the present disclosure provides a chess playing ability evaluation method, including:
pushing the chess board to a display interface for displaying;
searching and determining N recommended falling points according to a preset search breadth based on the current chess board, and sequencing the N recommended falling points to obtain a sequencing result, wherein N is more than or equal to 1;
receiving the drop information of a target user, and grading the drop information based on the sequencing result to obtain a grading result;
searching the chess board after the target user falls according to the preset searching breadth, determining a target falling point with the minimum deviation with a set winning rate value, and performing the next step after the target user falls according to the target falling point;
and responding to the determination of meeting the playing ending condition, acquiring at least one scoring result corresponding to at least one piece of drop information of the target user, and evaluating the target user according to the at least one scoring result to obtain an evaluation result.
In some exemplary embodiments, the searching based on the current playing board according to the predetermined search breadth to determine N recommended drop points, and sorting the N recommended drop points to obtain a sorting result includes:
searching a Monte Carlo tree obtained in advance according to the preset search breadth by using a strategy model trained in advance based on the current chess board, and determining N recommended child falling points;
and sequencing the N recommended drop points by utilizing a victory rate prediction model obtained by pre-training and the Monte Carlo tree to obtain a sequencing result.
In some exemplary embodiments, the sorting the N recommended falling points to obtain a sorting result includes:
determining N corresponding first odds for the N recommended drop points by using a pre-trained odds prediction model;
and sequencing the N recommended drop points according to the N first wins to obtain the sequencing result.
In some exemplary embodiments, the receiving the fall information of the target user, and scoring the fall information based on the ranking result to obtain a scoring result includes:
determining the first N1 recommended drop points in the ranking result as top-ranked drop points, wherein the remaining N2 recommended drop points except the first N1 recommended drop points in the ranking result are secondary-ranked drop points, wherein N1+ N2 is equal to N;
comparing the received falling information of the target user with the superior falling point and the inferior falling point;
in response to the fact that the falling information belongs to the superior falling point, obtaining a scoring result G-M of the falling information, wherein M is the highest scoring value, and G is larger than or equal to 0;
and in response to the fact that the falling information belongs to the secondary falling points, acquiring a sorting sequence n of the falling information in the secondary falling points, and obtaining a grading result G-n M of the falling information, wherein M is a preset interval score.
In some exemplary embodiments, the receiving the fall information of the target user, and scoring the fall information based on the ranking result to obtain a scoring result includes:
and in response to the fact that the received falling information of the target user is determined to be in the sorting result, determining that the sorting sequence of the falling information in the sorting result is A, and obtaining a scoring result G-M-A M/N of the falling information, wherein M is the highest scoring value, and G is larger than or equal to 0.
In some exemplary embodiments, the receiving the fall information of the target user, and scoring the fall information based on the ranking result to obtain a scoring result further includes:
in response to determining that the received fall information of the target user is not within the ranking result, a scoring result G-L of the fall information is obtained.
In some exemplary embodiments, the searching, according to the predetermined search breadth, the playing board based on the target user who fell down determines a target fall point having a minimum deviation from a set winning rate value, and the next falling after the target user fell down is performed according to the target fall point, including:
searching and determining at least one point to be fallen based on the chess board after the target user falls according to the preset searching breadth;
determining at least one corresponding second winning rate for the at least one to-be-dropped sub-point by utilizing a pre-trained winning rate prediction model;
and screening out a target second winning rate with the minimum deviation from the set winning rate value from the at least one second winning rate, taking a point to be dropped corresponding to the target second winning rate as a target dropping point, and performing next dropping after the target user is dropped according to the target dropping point.
In some exemplary embodiments, the training of the win ratio prediction model comprises:
obtaining a preset number of chess board samples, marking the actual win rate of each alternative fall point of each chess board sample in advance, wherein one chess board sample corresponds to a plurality of alternative fall points;
constructing an initial neural network, inputting the marked chess board samples into the initial neural network for processing, and outputting the output success rate of each alternative drop corresponding to the marked chess board samples;
determining a loss function according to the deviation of the output rate and the real rate in the mark, and adjusting the internal parameters of the initial neural network according to the loss function until the output rate is the same as the real rate in the mark;
and determining the initial neural network as a winning rate prediction model after the initial neural network completely processes the predetermined number of marked chess board samples.
In some exemplary embodiments, the play end condition includes at least one of:
and determining that the target user wins and fails when the playing accumulated time reaches the set time and the target user accumulated falling times reaches the preset times.
In some exemplary embodiments, further comprising:
and determining a target course corresponding to the target user according to the evaluation result, and recommending the target course to the terminal equipment of the target user.
Based on the same inventive concept, the exemplary embodiment of the present disclosure further provides an evaluation device for chess playing ability, including:
the chessboard pushing module is configured to push the chessboard to the display interface to be displayed;
the chess searching and sorting module is configured to search and determine N recommended chess falling points according to a preset searching breadth based on a current chess board, sort the N recommended chess falling points and obtain a sorting result, wherein N is more than or equal to 1;
the scoring module is used for receiving the drop information of the target user and scoring the drop information based on the sorting result to obtain a scoring result;
the intelligent falling module is configured to search according to the preset search breadth on the basis of a chess board after the target user falls, determine a target falling point with the minimum deviation with a set winning rate value, and perform the next falling after the target user falls according to the target falling point;
and the evaluation module is configured to respond to the determination that the playing ending condition is met, acquire at least one scoring result corresponding to at least one piece of drop information of the target user, and evaluate the target user according to the at least one scoring result to obtain an evaluation result.
Based on the same inventive concept, exemplary embodiments of the present disclosure also provide an electronic device, including: the evaluation system comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein the processor realizes the evaluation method when executing the computer program.
Based on the same inventive concept, the disclosed exemplary embodiments also provide a non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the evaluation method of any one of the above.
From the above, it can be seen that, according to the evaluation method, the apparatus, the electronic device and the storage medium for playing chess provided by the present disclosure, the initial chess playing board is firstly presented to the target user through the display interface, the target user can play chess on the basis of the initial chess playing board, once a piece is played by the target user, the evaluation is performed according to the piece dropping situation of the target user, then the artificial intelligence system performs the next piece dropping according to the piece dropping situation of the target user, so that the target user can play chess with the artificial intelligence system, and the evaluation result of the target user is obtained by combining the evaluation results of the multiple piece dropping of the target user after the chess playing is completed. Therefore, the chess playing ability of the target user can be determined according to the evaluation result, and a proper teacher can be selected for the target user better according to the chess playing ability of the target user, and a corresponding training course can be made. The scheme disclosed only needs the target user and the artificial intelligence system to play chess once based on a chess board of playing chess, and the artificial intelligence system can automatically adjust the position of a child point on the next step based on the child condition after the target user falls at every turn, so that the accuracy can be guaranteed, the time for chess strength evaluation can be reduced, and the chess is convenient to use.
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In order to more clearly illustrate the technical solutions in the present disclosure or related technologies, the drawings needed to be used in the description of the embodiments or related technologies are briefly introduced below, and it is obvious that the drawings in the following description are only embodiments of the present disclosure, and for those skilled in the art, 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 chart diagram of a method for evaluating chess playing ability according to an exemplary embodiment of the present disclosure;
FIG. 3 is a schematic diagram of a training process of a win ratio prediction model according to an exemplary embodiment of the present disclosure;
FIG. 4 is an expanded view of the process of step 204 in FIG. 2;
FIG. 5 is another schematic flow chart diagram of a method for evaluating chess playing ability according to an exemplary embodiment of the present disclosure;
fig. 6 is a schematic structural view of an evaluation device for chess playing ability according to an exemplary embodiment of the present disclosure;
fig. 7 is another schematic structural view of an evaluation device for chess playing ability according to an exemplary embodiment of the present disclosure;
fig. 8 is a schematic view of still another structure of an evaluation apparatus for playing chess capability according to an exemplary embodiment of the present disclosure;
fig. 9 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 disclosure will be described with reference to a number of exemplary embodiments. It is understood that these embodiments are given solely for the purpose of enabling those skilled in the art to better understand and to practice the present disclosure, and are not intended to limit the scope of the present disclosure 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 chess playing capability evaluation method, a chess playing capability evaluation device, electronic equipment and a storage medium 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.
Win ratio prediction model (ValueNet, estimation network): the method is a neural network model which is obtained by training a neural network through a large number of samples and can predict the win ratio value of various fellows in the next step based on the current state of the chessboard. The neural network has the characteristics of a neural network and has certain self-learning capability.
Policy model (policyenet, policy network): after training (learning) is performed by using the neural network in advance, screening and determination of the next step of the falling point can be performed based on the current chessboard.
Monte Carlo Tree (Monte Carlo Tree Search): the heuristic search algorithm is based on a tree data structure and is still effective under the condition of huge search space.
Chess playing capacity: mainly refers to the level of chess playing of a person, and is used as a basis for grading the chess playing level of the person.
The principles and spirit of the present disclosure are explained in detail below with reference to several representative embodiments of the present disclosure.
Summary of The Invention
The scheme of the disclosure aims to provide a chess playing ability evaluation method, a chess playing ability evaluation device, electronic equipment and a storage medium, so as to realize a scheme for quickly and accurately evaluating the chess playing ability of a user. Wherein, the chess can be go, Chinese chess, gobang, army chess and four-country battle, dark chess, Chinese checkers, black and white chess, six-piece chess, flying chess, general chess, beast chess, etc.
The scheme of the disclosure is particularly suitable for the weiqi in the above various kinds of weiqi. The playing method of the go is varied, and the rules for evaluating the victory or defeat are different, so the evaluation of the playing ability of the go is relatively complex. The present disclosure generally relates to the use of an I (Artificial Intelligence) engine for go in the go direction. With the development of artificial intelligence technology, the I engine of the go is widely applied to the education products of the go, such as man-machine chess playing, chess manual analysis, situational analysis, final outcome judgment of the go, and the like. The I engine of the go aims at providing an AI player matched with the level of a user and a result of the copy meeting the teaching purpose of a teacher. The traditional I-go engine (such as AlphaGo) aims at achieving a high I-go level, and in the scene of learning I-go, the learner has a weak level and is difficult to obtain the pleasure of a chess joint opponent in the process of playing with high-level I-go; meanwhile, the I engine of the go can carry out self-training in a self-playing mode without the need of human beings to provide prior knowledge. The human learner usually is guided by the teacher, plays the game after learning, and the teacher plays the game again after playing the game so as to complete the learning goal. The methods of the I-go engine and the human learning of the I-go are quite different, and the knowledge of the I-go is difficult to apply directly.
Based on the above description and the problems existing in the prior art, the present disclosure provides a method, an apparatus, an electronic device, and a storage medium for evaluating chess playing capability, in which an initial chess playing board is first presented to a target user through a display interface, the target user can perform a chess-playing based on the initial chess playing board, the target user can perform a scoring once according to the chess-playing situation of the target user when the target user performs a chess-playing, and then an artificial intelligence system performs a next chess-playing according to the chess-playing situation of the target user, so that the target user can play chess with an artificial intelligence system, and the scoring result of multiple chess-playing of the target user is combined after the chess-playing is completed, so as to obtain the evaluation result of the target user. Therefore, the chess playing ability of the target user can be determined according to the evaluation result, and a proper teacher can be selected for the target user better according to the chess playing ability of the target user, and a corresponding training course can be made. The scheme disclosed only needs the target user and the artificial intelligence system to play chess once based on a chess playing chessboard, and the artificial intelligence system can automatically adjust the position of a dropping point on the next step based on the dropping condition after the target user drops each time, so that the accuracy of the target user can be guaranteed, the chess force evaluation time can be reduced, and the chess force evaluation system is convenient to use.
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
Reference is made to fig. 1, which is a schematic view of an application scenario of the evaluation method for chess playing ability according to 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 evaluation service of chess playing capability for a target user of the terminal device 101, and a client communicated with the server 102 is installed in the terminal device 101.
First, the server 102 transmits a predefined game board to the client of the terminal device 101 via the communication network, displays the game board on a display interface corresponding to the client, searches for recommended drop points based on the current game board, and sorts the recommended drop points. The target user plays chess according to the chess playing chessboard displayed on the display interface, the client uploads the falling situation of the target user to the server 102 through the communication network, the server 102 scores according to the falling situation of the target user and the sorted recommended falling points, and the scoring result can be stored in the data storage system 103.
Then, the server 102 selects the next drop point according to the drop condition of the target user, and sends the next drop point to the client of the terminal device 101 through the communication network, and displays the next drop point through the display interface.
The above processes are repeated continuously to realize the playing process of the target user and the artificial intelligence, when the playing ending condition is met, the server 102 calls all scoring results of the target user playing the game from the data storage system 103, integrates all scoring results to obtain an evaluation result, and the server 102 pushes the evaluation result to the client of the terminal device 101 through the communication network and displays the evaluation result through the display interface. And further completing the task of evaluating the chess playing capability of the target user.
The method for evaluating chess playing ability according to the exemplary embodiment of the present disclosure is described below with reference to an 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
Referring to fig. 2, an embodiment of the present disclosure provides a method for evaluating chess playing ability, including the following steps:
In specific implementation, the playing board may be an empty board or a preset board game with certain difficulty, and how to set the board game may be set according to the type of the corresponding board game and the basic information (such as age and calendar) of the target user. In addition, a plurality of chess games with different difficulty degrees can be preset, and the target user can select the chess game which is most suitable for the target user from the plurality of chess games as the playing chess according to the actual situation of the target user.
For example, the type of the chess is go, and an initial chess board for performing the evaluation with the target user on a 200-handed chess board may be set in advance.
In specific implementation, the process of selecting recommended drop points includes:
at the beginning, searching is carried out on a pre-obtained Monte Carlo tree according to the preset searching breadth by utilizing a strategy model trained in advance based on the pushed chess board, and N recommended child falling points are determined. The predetermined search width may be set according to actual needs, for example, (analysiswideorotnoise) [0, 1], and the search width is narrower as the numerical value is closer to 0; the closer the value is to 1, the wider the search breadth. The predetermined search scope is increased, so that the search range can be enlarged, the weight of the access times of each node on the monte carlo tree given by the ucb (upper confidence bound) value during searching is increased, and the access opportunity of the node with less access times in the monte carlo tree is increased.
In specific implementation, before sorting the N recommended drop points, a probability prediction model is obtained by using neural network training, as shown in fig. 3, the training process is as follows:
In specific implementation, the obtaining process of the chess board samples comprises the following steps:
1. and (4) randomly initializing the playing model and continuously training the playing model.
2. The current strongest playing model and the second strongest playing model are used for self-playing, and a certain number of playing chessboard samples are generated.
3. And (3) training the strongest playing model in the step (2) by using the generated playing chessboard samples to generate a new playing model.
4. And (4) checking whether the strength of the new playing model is greater than that of the current strongest playing model, if so, jumping to 5, and if not, jumping to 2.
5. And (3) taking the new playing model as a new strongest playing model and the old strongest playing model as a second strongest playing model, and jumping to the step (2).
And (5) continuously repeating the self-playing process of the steps 2 to 5 to obtain a large number of playing chessboard samples, and then carrying out win rate marking on a plurality of alternative drops corresponding to each playing chessboard sample to obtain a training sample.
And 303, inputting the marked chess board samples into an initial neural network for processing, and outputting the output success rate of each alternative drop corresponding to the marked chess board samples.
In specific implementation, the marked chess board samples are input through the input layer, enter each hidden layer to be subjected to layered processing, finally obtain the output success rate of each alternative drop, and output after the output success rates of the alternative drops are integrated by the output layer.
And step 304, determining a loss function according to the deviation between the output rate and the real rate in the mark, and adjusting the internal parameters of the initial neural network according to the loss function until the output rate is the same as the real rate in the mark.
In specific implementation, the larger the loss value of the loss function is, the larger the deviation from the true win ratio is proved to be, so that the parameters of each layer of the initial neural network can be adjusted according to the size of the loss value until the obtained loss value of the loss function meets the requirement (that is, the output win ratio is the same as the true win ratio in the label). Thus, the training of the chess board sample is completed.
And then continuously selecting the next game board sample, repeating the processes of the step 303 and the step 304 until all game board samples are completely trained, and entering the step 305.
In specific implementation, the steps 2 to 5 in the process of obtaining the chess board samples can be continuously repeated to obtain a batch of new chess board samples as test samples, and the win ratio prediction model is tested. If the accuracy obtained by the test result does not reach the corresponding accuracy threshold value, the steps 2 to 5 in the process of obtaining the chess board samples can be repeated again to obtain a batch of new chess board samples as training samples to train the victory ratio prediction model again until the accuracy of the victory ratio prediction model obtained after the test samples are tested reaches the accuracy threshold value. Therefore, the accuracy of the success rate prediction model can be ensured, and the use value is improved.
Based on the above process, the process of sorting the N recommended drop points includes the following two cases:
the first sorting mode: and sequencing the N recommended drop points by utilizing a victory rate prediction model obtained by pre-training and the Monte Carlo tree to obtain a sequencing result.
In specific implementation, the obtained N recommended drops pass through a win rate prediction model to obtain a win rate X (X) corresponding to each recommended drop 1 、X 2 ……X N ). Obtaining the corresponding access times Y (Y) of the N recommended drops in the Monte Carlo tree 1 、Y 2 ……Y N ). And appropriate weights are given to the obtained X and Y, so that the X and Y can be combined to determine a sorting result, and the obtained sorting result does not simply depend on the winning rate and the visiting times, so that the sorting result is more in line with the requirements and the chess playing habits of the actual users.
The second sorting mode: determining N corresponding first odds for the N recommended drop points by using a pre-trained odds prediction model; and sequencing the N recommended drop points according to the N first odds to obtain the sequencing result.
The second sorting mode is purely sorting by means of winning rates, and the sorting operation is simple and quick.
And 203, receiving the drop information of the target user, and scoring the drop information based on the sequencing result to obtain a scoring result.
The scoring method is divided into two types:
the first scoring mode is as follows:
determining the first N1 recommended drop points in the sorting result as top-grade drop points, wherein the rest N2 recommended drop points except the first N1 recommended drop points in the sorting result are secondary-grade drop points, and N1+ N2 is equal to N.
And comparing the received falling information of the target user with the superior falling point and the inferior falling point.
And responding to the determination that the fall information belongs to the superior fall point, and obtaining a scoring result G-M of the fall information, wherein M is the highest scoring value, and G is more than or equal to 0. For example, when the target user's fall point is within the top 5 hand's top fall point, N1 equals 5, the score result is G equals 100.
And in response to the fact that the falling information belongs to the secondary falling points, acquiring a sorting sequence n of the falling information in the secondary falling points, and obtaining a grading result G-n M of the falling information, wherein M is a preset interval score. And if the value of G is less than 0, directly determining that G is equal to 0. For example, if the predetermined interval score is chosen to be 20, the score G will be decremented by 20 each subsequent order of the target user.
In addition, the sorting result may also be divided into three, four or more categories, and the like, which may be specifically set according to the needs of the user. Corresponding scoring values can be given to all the categories, and if the falling information falls into the corresponding categories, the corresponding scoring values can be directly given.
In response to determining that the received fall information of the target user is not within the ranking result, a scoring result G-L of the fall information is obtained. L is the lowest score, e.g., let L be 0.
By the scheme, the falling information can be scored according to different categories, so that the scoring result is more in line with the actual requirement.
The second scoring mode is as follows:
and in response to the fact that the received falling information of the target user is determined to be in the sorting result, determining that the sorting sequence of the falling information in the sorting result is A, and obtaining a scoring result G-M-A M/N of the falling information, wherein M is the highest scoring value, and G is larger than or equal to 0.
In response to determining that the received fall information of the target user is not within the ranking result, a scoring result G-L of the fall information is obtained. L is the lowest score, e.g., let L be 0.
By the scheme, the scoring is directly performed according to the sequence of the sequencing results, the scoring mode is simple and quick, and the occupied processing space is small.
And 204, searching the chess board after the target user falls according to the preset search breadth, determining a target fall point with the minimum deviation with the set winning rate value, and performing the next step after the target user falls according to the target fall point.
In specific implementation, the set win ratio is less than 100%, the set win ratio of the present disclosure is preferably 50%, and the determined win ratio of the target drop point is closer to 50%, so that dropping according to the target drop point does not fail and does not win, and the test can be performed by always keeping the same level as that of the target user, thereby enabling the test result to be more accurate. Of course, the set win ratio value may be adjusted according to actual conditions, for example, 56% or 60%.
The specific deployment process of step 204, as shown in fig. 4, includes:
In specific implementation, the playing chessboard after the target user falls is used as the current playing chessboard, and based on the current playing chessboard, the process of determining at least one point to be fallen can be the same as the selection process of the corresponding recommended falling point.
The method specifically comprises the following steps: and searching on a Monte Carlo tree obtained in advance according to a preset search breadth by using a pre-trained strategy model based on the current chess board, and determining at least one to-be-dropped child point.
And if only one point to be dropped is determined, directly taking the point to be dropped as a target dropping point.
If the number of the waiting dots is determined to be plural, the following steps 402 and 403 can be performed.
And 402, determining at least one corresponding second winning rate for the at least one to-be-fallen sub-point by utilizing a pre-trained winning rate prediction model.
In specific implementation, the win ratio prediction model is obtained by training according to steps 301 to 305 corresponding to fig. 3. Therefore, a second win rate corresponding to each point to be fallen can be determined for each point to be fallen by using the win rate prediction model based on the current chess board, and the determined second win rate is more accurate.
And 403, screening out a target second winning rate with the minimum deviation from the set winning rate value from the at least one second winning rate, taking a to-be-dropped point corresponding to the target second winning rate as a target dropped point, and performing next dropping after the target user is dropped according to the target dropped point.
In specific implementation, if a plurality of second odds with the smallest deviation from the set odds value are screened out, the selection can be performed according to the access times of the plurality of to-be-dropped dots corresponding to the second odds, and the to-be-dropped dot with the highest access time is selected as the target dropped dot. Or, a point to be dropped can be randomly screened and determined from the target dropping point, and the target dropping point can be specifically set according to actual needs.
The chess playing process of the target user and the artificial intelligence system is repeated continuously, so that the artificial intelligence system can score according to the situation of each time the target user falls, and then a plurality of scoring results are obtained.
Wherein, the playing end condition includes but is not limited to at least one of the following: and determining that the target user wins and fails when the playing accumulated time reaches the set time and the target user accumulated falling times reaches the preset times. For example, for go, 200 play is set to end the post-evaluation.
The playing end condition may be set according to actual requirements of various kinds of chess, and is not specifically limited herein.
In specific implementation, if the game ending condition is met, the evaluation is proved to be ended, all the evaluation results of the target user are integrated, and the average value of all the evaluation results is calculated and further used as the final evaluation result; or accumulating the scoring result and directly taking the accumulated result as the evaluation result.
Therefore, the chess playing ability of the target user can be evaluated according to the evaluation result, and therefore some training institutions can conveniently make corresponding learning plans for the target user and match proper teachers for guidance.
Based on the above process, the following steps can be performed after obtaining the evaluation result, as shown in fig. 5:
and step 206, determining a target course corresponding to the target user according to the evaluation result, and recommending the target course to the terminal equipment of the target user.
In specific implementation, the course information corresponding to various evaluation levels is stored in the server in advance, so that the evaluation level of the target user can be determined according to the evaluation result, the target course matched with the evaluation level of the target user is selected, and the target course is pushed to the terminal equipment of the target user. Therefore, the target user can watch and learn the target course on the terminal equipment, and if the target course is learned, the chess playing ability of the target user can be evaluated again according to the process, so that a new learning plan and a new learning course are made, and the chess playing ability of the target user can be continuously improved in learning.
Exemplary device
Referring to fig. 6, based on the same inventive concept as the above-mentioned test method for any chess playing ability, the disclosed embodiment further provides an evaluation device for chess playing ability.
The evaluation device of chess playing ability includes:
the chessboard pushing module 601 is configured to push the chessboard to the display interface for displaying;
a drop search sorting module 602, configured to search according to a predetermined search scope based on a current chess board to determine N recommended drop points, and sort the N recommended drop points to obtain a sorting result, where N is greater than or equal to 1;
the scoring module 603 is configured to receive the drop information of the target user, and score the drop information based on the sorting result to obtain a scoring result;
the intelligent falling module 604 is configured to search according to the preset search scope based on the chess board after the target user falls, determine a target falling point with the minimum deviation with the set winning rate value, and perform the next falling after the target user falls according to the target falling point;
and the evaluation module 605 is configured to respond to the determination that the playing ending condition is met, acquire at least one scoring result corresponding to at least one piece of drop information of the target user, and evaluate the target user according to the at least one scoring result to obtain an evaluation result.
In some optional embodiments, the colony search ranking module 602 is specifically configured to:
searching a Monte Carlo tree obtained in advance according to the preset search breadth by using a strategy model trained in advance based on the current chess board, and determining N recommended child falling points; and sequencing the N recommended drop points by utilizing a victory rate prediction model obtained by pre-training and the Monte Carlo tree to obtain a sequencing result.
In some optional embodiments, the drop search ranking module 602 is further specifically configured to:
determining N corresponding first odds for the N recommended drop points by using a pre-trained odds prediction model; and sequencing the N recommended drop points according to the N first wins to obtain the sequencing result.
In some optional embodiments, the scoring module 603 is specifically configured to:
determining the first N1 recommended drop points in the ranking result as top-ranked drop points, wherein the remaining N2 recommended drop points except the first N1 recommended drop points in the ranking result are secondary-ranked drop points, wherein N1+ N2 is equal to N; comparing the received falling information of the target user with the superior falling point and the inferior falling point; in response to the fact that the falling information belongs to the superior falling point, obtaining a scoring result G-M of the falling information, wherein M is the highest scoring value, and G is larger than or equal to 0; and in response to the fact that the falling information belongs to the secondary falling points, acquiring a sorting sequence n of the falling information in the secondary falling points, and obtaining a grading result G-n M of the falling information, wherein M is a preset interval score.
In some optional embodiments, the scoring module 603 is further specifically configured to: and in response to the fact that the received falling information of the target user is determined to be in the sorting result, determining that the sorting sequence of the falling information in the sorting result is A, and obtaining a scoring result G-M-A M/N of the falling information, wherein M is the highest scoring value, and G is larger than or equal to 0.
In some optional embodiments, the scoring module 603 is further specifically configured to: in response to determining that the received fall information of the target user is not within the ranking result, a scoring result G-L of the fall information is obtained.
In some optional embodiments, the intelligent drop module 604 is specifically configured to:
searching and determining at least one point to be fallen based on the chess board after the target user falls according to the preset searching breadth; determining at least one corresponding second winning rate for the at least one to-be-dropped sub-point by utilizing a pre-trained winning rate prediction model; and screening out a target second winning rate with the minimum deviation from the set winning rate value from the at least one second winning rate, taking a point to be dropped corresponding to the target second winning rate as a target dropping point, and performing next dropping after the target user is dropped according to the target dropping point.
In some alternative embodiments, as shown in fig. 7, the apparatus further comprises: a model training module 606 configured to:
obtaining a preset number of chess board samples, and marking the winning rate according to the actual winning rate of each alternative falling point of each chess board sample in advance, wherein one chess board sample corresponds to a plurality of alternative falling points; constructing an initial neural network, inputting the marked chess board samples into the initial neural network for processing, and outputting the output success rate of each alternative drop corresponding to the marked chess board samples; determining a loss function according to the deviation of the output rate and the real rate in the mark, and adjusting the internal parameters of the initial neural network according to the loss function until the output rate is the same as the real rate in the mark; and determining the initial neural network as a winning rate prediction model after the initial neural network completely processes the predetermined number of marked chess board samples.
In some optional embodiments, the play end condition includes at least one of:
and determining that the target user wins and fails when the playing accumulated time reaches the set time and the target user accumulated falling times reaches the preset times.
In some alternative embodiments, as shown in fig. 8, the apparatus further comprises:
the course recommending module 607 is configured to determine a target course corresponding to the target user according to the evaluation result, and recommend the target course to the terminal device of the target user.
For convenience of description, the above devices are described as being divided into various modules by functions, and are described separately. Of course, the functionality of the various modules may be implemented in the same one or more pieces of software and/or hardware in practicing the present disclosure.
The device of the above embodiment is used for implementing the corresponding evaluation method of chess playing ability in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which are not described herein again.
Based on the same inventive concept, corresponding to the method of any embodiment, the present disclosure further provides an electronic device, which includes a memory, a processor, and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the computer program to implement the evaluation method of chess playing capability according to any embodiment.
Fig. 9 is a schematic diagram illustrating a more specific hardware structure of an electronic device according to this embodiment, where the electronic device may include: a processor 910, a memory 920, an input/output interface 930, a communication interface 940, and a bus 950. Wherein the processor 910, the memory 920, the input/output interface 930, and the communication interface 940 are communicatively coupled to each other within the device via a bus 950.
The processor 910 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 920 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 920 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 920 and called by the processor 910 to be executed.
The input/output interface 930 is used for connecting an input/output module to realize information input and output. The i/o module may be configured as a component in a device (not shown) or may be external to the device to provide a corresponding function. The input devices may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output devices may include a display, a speaker, a vibrator, an indicator light, etc.
The communication interface 940 is used for connecting a communication module (not shown in the figure) to implement communication interaction between the present device and other devices. 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 910, the memory 920, the input/output interface 930, the communication interface 940 and the bus 950, 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 the components necessary to implement the embodiments of the present disclosure, and need not include all of the components shown in the figures.
The electronic device of the above embodiment is used for implementing the evaluation method of the chess playing ability corresponding to any one of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which are not described herein again.
Exemplary program product
Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, the present disclosure also provides a non-transitory computer-readable storage medium storing computer instructions for causing the computer to execute the evaluation method of chess playing ability according to any of the above 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 computer instructions stored in the storage medium of the above embodiment are used to enable the computer to execute the evaluation method for chess playing capability according to any one of the above exemplary method embodiments, and have 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, which may be 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.
Claims (13)
1. A chess playing ability evaluation method comprises the following steps:
pushing the playing chessboard to a display interface for displaying, wherein a plurality of chess games with different difficulty degrees are preset, and a target user selects a corresponding chess game from the plurality of chess games as the playing chessboard according to the actual situation of the target user;
searching and determining N recommended falling points according to a preset search breadth based on the current chess board, and sequencing the N recommended falling points to obtain a sequencing result, wherein N is more than or equal to 1;
receiving the drop information of a target user, and grading the drop information based on the sequencing result to obtain a grading result;
searching the chess board after the target user falls according to the preset searching breadth, determining a target falling point with the minimum deviation with a set winning rate value, and performing the next step after the target user falls according to the target falling point;
and responding to the determination of meeting the playing ending condition, acquiring at least one scoring result corresponding to at least one piece of drop information of the target user, and evaluating the target user according to the at least one scoring result to obtain an evaluation result.
2. The evaluation method according to claim 1, wherein the searching for determining N recommended fall points according to a predetermined search breadth based on the current playing board, and the ranking of the N recommended fall points to obtain a ranking result comprises:
searching a Monte Carlo tree obtained in advance according to the preset search breadth by using a strategy model trained in advance based on the current chess board, and determining N recommended child falling points;
and sequencing the N recommended drop points by utilizing a victory rate prediction model obtained by pre-training and the Monte Carlo tree to obtain a sequencing result.
3. The evaluation method of claim 1, wherein the sorting the N recommended falling points to obtain a sorting result comprises:
determining N corresponding first odds for the N recommended drop points by using a pre-trained odds prediction model;
and sequencing the N recommended drop points according to the N first wins to obtain the sequencing result.
4. The evaluation method of claim 1, wherein the receiving of the landing information of the target user and the scoring of the landing information based on the sorting result to obtain a scoring result comprises:
determining the first N1 recommended drop points in the ranking result as top-ranked drop points, wherein the remaining N2 recommended drop points except the first N1 recommended drop points in the ranking result are secondary-ranked drop points, wherein N1+ N2 is equal to N;
comparing the received falling information of the target user with the superior falling point and the inferior falling point;
in response to the fact that the falling information belongs to the superior falling point, obtaining a scoring result G-M of the falling information, wherein M is the highest scoring value, and G is larger than or equal to 0;
and in response to the fact that the falling information belongs to the secondary falling points, acquiring a sorting sequence n of the falling information in the secondary falling points, and obtaining a grading result G-n M of the falling information, wherein M is a preset interval score.
5. The evaluation method of claim 1, wherein the receiving of the landing information of the target user and the scoring of the landing information based on the sorting result to obtain a scoring result comprises:
and in response to the fact that the received falling information of the target user is determined to be in the sorting result, determining that the sorting sequence of the falling information in the sorting result is A, and obtaining a scoring result G-M-A M/N of the falling information, wherein M is the highest scoring value, and G is larger than or equal to 0.
6. The evaluation method according to claim 4 or 5, wherein the receiving of the landing information of the target user and the scoring of the landing information based on the ranking result to obtain a scoring result further comprises:
in response to determining that the received fall information of the target user is not within the ranking result, a scoring result G-L of the fall information is obtained.
7. The evaluation method according to claim 1, wherein the searching based on the target user fallen chess board according to the predetermined search breadth, determining a target fall point having a minimum deviation from a set winning rate value, and performing a next target fall after the target user fallen according to the target fall point, comprises:
searching and determining at least one point to be fallen based on the chess board after the target user falls according to the preset searching breadth;
determining at least one corresponding second winning rate for the at least one to-be-dropped sub-point by utilizing a pre-trained winning rate prediction model;
and screening out a target second winning rate with the minimum deviation from the set winning rate value from the at least one second winning rate, taking a point to be dropped corresponding to the target second winning rate as a target dropping point, and performing next dropping after the target user is dropped according to the target dropping point.
8. An evaluation method according to claim 2, 3 or 7, wherein the training of the win ratio prediction model comprises:
obtaining a preset number of chess board samples, and marking the winning rate according to the actual winning rate of each alternative falling point of each chess board sample in advance, wherein one chess board sample corresponds to a plurality of alternative falling points;
constructing an initial neural network, inputting the marked chess board samples into the initial neural network for processing, and outputting the output success rate of each alternative drop corresponding to the marked chess board samples;
determining a loss function according to the deviation of the output rate and the real rate in the mark, and adjusting the internal parameters of the initial neural network according to the loss function until the output rate is the same as the real rate in the mark;
and determining the initial neural network as a winning rate prediction model after the initial neural network completely processes the predetermined number of marked chess board samples.
9. The evaluation method according to claim 1, wherein the play end condition includes at least one of:
and determining that the target user wins and fails when the playing accumulated time reaches the set time and the target user accumulated falling times reaches the preset times.
10. The evaluation method according to claim 1, further comprising:
and determining a target course corresponding to the target user according to the evaluation result, and recommending the target course to the terminal equipment of the target user.
11. An evaluation device for chess playing ability, comprising:
the chess board pushing module is configured to push the chess boards to the display interface for displaying, wherein a plurality of chess games with different difficulty degrees are preset, and the target user selects the corresponding chess game from the plurality of chess games as the chess board according to the actual condition of the target user;
the chess searching and sorting module is configured to search and determine N recommended chess falling points according to a preset searching breadth based on a current chess board, sort the N recommended chess falling points and obtain a sorting result, wherein N is more than or equal to 1;
the scoring module is used for receiving the drop information of the target user and scoring the drop information based on the sorting result to obtain a scoring result;
the intelligent falling module is configured to search according to the preset search breadth on the basis of a chess board after the target user falls, determine a target falling point with the minimum deviation with a set winning rate value, and perform the next falling after the target user falls according to the target falling point;
and the evaluation module is configured to respond to the determination that the playing ending condition is met, acquire at least one scoring result corresponding to at least one piece of drop information of the target user, and evaluate the target user according to the at least one scoring result to obtain an evaluation result.
12. An electronic device, comprising: memory, processor and computer program stored on the memory and executable on the processor, the processor implementing the evaluation method according to any one of claims 1 to 10 when executing the computer program.
13. A non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the assessment method of any one of claims 1 to 10.
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