CN107622092B - Searching method of the Chinese chess based on Multiple Optimization, Iterative deepening beta pruning - Google Patents
Searching method of the Chinese chess based on Multiple Optimization, Iterative deepening beta pruning Download PDFInfo
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Abstract
The invention discloses the Chinese chesses searching method based on Multiple Optimization, Iterative deepening beta pruning, this method is the method that series of optimum search has been superimposed on the basis of traditional Alpha-Beta pruning search algorithm, greatly improves the beta pruning efficiency and search accuracy rate of traditional Alpha-Beta algorithm.Include: that the Iterative deepening search technique of (1) design can make full use of computing resource, increases beta pruning probability；(2) beginning library, which is added, makes system initially be provided with the style of moving of profession in chess game；(3) state machine search mechanisms are constructed, optimize each method executes sequence, and the thinking of machine is made more to have logic；(4) the outstanding way to get there obtained, which is recorded in history lists, makes system have stronger learning ability.Inventive algorithm and Optimization Mechanism guarantee artificial intelligence (Artificial Intelligent, AI) algorithm can intelligently carry out man-machine battle, and resource space occupancy is small, and it is simple and convenient, it is a kind of artificial intelligence Chinese chess searching method of lightweight for having efficient AI algorithm.
Description
Technical field
The present invention relates to the Chinese chesses searching method based on Multiple Optimization, Iterative deepening beta pruning, belongs to Alpha-Beta
Pruning search technical field.
Background technique
Computer has also bred the generation of artificial intelligence technology while birth, and artificial intelligence technology be initially exactly from as
Practice explores in chess program, therefore speaks of artificial intelligence technology, people will necessarily be allowed to expect Chinese chess.Intelligent Chinese chess is just made
In order to which people study the important carrier of artificial intelligence, researching value is no less than the drosophila of living nature, and how to make Chinese chess system
Unite it is more intelligent, efficient be people research target.At present intelligence Chinese chess system during playing chess often
The searching algorithm efficiency for reacting slower and traditional is lower, and consuming stock number is big, and the lower son that can not efficiently provide chess piece is walked
Method is badly in need of the treatment effeciency that a kind of efficient lightweight searching algorithm carrys out lifting system.
Alpha-Beta algorithm is common game class searching algorithm, mainly passes through Alpha beta pruning and Beta beta pruning pair
Game theory scans for.The current optimal result obtained according to upper one layer, determines whether current search will continue.
This algorithm has many advantages, such as to realize that easy, precision is high, but the algorithm depends critically upon the searching sequence of way to get there.If you are always
First removal search it is the worst method, then Beta truncation would not occur, therefore the algorithm is the same just as min-max, efficiency
It is very low, entire game theory may finally be sought all over.To sum up, it designs a kind of based on Alpha-Beta pruning search algorithm, utilization
Position chessboard expression, the artificial intelligence Chinese chess searching method of the technologies such as beginning library, history lists, permutation table Optimizing Search algorithm,
It is our current problems to be solved.
Summary of the invention
It is searched the technical problems to be solved by the present invention are: providing Chinese chess based on Multiple Optimization, Iterative deepening beta pruning
Suo Fangfa, has that search effect is good, reaction speed is fast, cost at the shortcomings that overcoming traditional Alpha-Beta pruning search algorithm
The features such as low, easy extension.
The present invention uses following technical scheme to solve above-mentioned technical problem:
Searching method of the Chinese chess based on Multiple Optimization, Iterative deepening beta pruning, includes the following steps:
Step 1, for current situation, permutation table, killer's way to get there table and history lists are emptied, initialization control begins the time
Timer；
Step 2, search, which is played a game, starts the preceding beginning library being arranged, if the correspondence for searching current situation in beginning library is walked
Method is then begun according to search result, and terminates to search for；Otherwise enter in next step；
Step 3, all ways to get there of current situation are obtained according to Chinese chess rule, and whether judge way to get there unique, if uniquely,
Begin according to unique way to get there, and terminates to search for；Otherwise enter in next step；
Step 4, the horizontal line i=1 of setting Iterative deepening search, limit depth 32 change from depth 1 to depth i
In generation, deepens search；
Step 5, by search when encounter Beta truncation or Alpha truncation situation check code, depth, mark, score value,
Way to get there is recorded in permutation table；Search for permutation table, if finding current situation in permutation table, judge depth whether and current depth
Unanimously, if unanimously, virtually beginning to form new situation using the way to get there in permutation table, going to step 8, otherwise enter in next step；
Step 6, two ways to get there classic in the corresponding all ways to get there of current depth as killer's way to get there and are recorded in
In killer's way to get there table, judge whether the corresponding killer's way to get there of current depth is effective, virtually begun if effectively using killer's way to get there,
8 are gone to step, is otherwise entered in next step；
Step 7, all ways to get there are generated to current situation, exclude way to get there and killer's way to get there in permutation table, according to going through
Score value in history table is ranked up remaining way to get there, is virtually begun according to the way to get there of sequence first, into next step；
Step 8, judge whether the depth of search reaches current iteration and deepen the horizontal line i of search, if so, 9 are gone to step,
Otherwise 10 are gone to step；
Step 9, when the depth of Iterative deepening search reaches horizontal line i, static search is carried out, when static state search terminates
Afterwards, whether determined level line i reaches the limit of depth, if then going to step 14, otherwise, i=i+1, and return step 4；
Step 10, judge whether to generate and repeat situation, if without situation is repeated, into next step；
Step 11, it attempts empty step to cut, and enters in next step；
Step 12, judge whether to have killed chess, stop current iteration if having killed chess and deepen search, return step 4 is opened
Beginning next iteration deepens search, otherwise enters in next step；
Step 13, the way to get there found in step 5 to step 7 is recorded in permutation table and history lists, determined level line i is
No to reach the limit of depth, searching for if reaching terminates, and goes to step 14；Otherwise i=i+1 attempts step 5 respectively and looks for into step 7
To the way to get there empty step in step 11 of virtually beginning or execute cut, virtual switch walks son side, and progress recurrence is moved, return step
4；
Step 14, entire search process terminates, and begins by final search result, and exchanges son side, begins to player.
As a preferred solution of the present invention, beginning library described in step 2 by Zobrist situation check code, way to get there description,
Way to get there score value composition, and a situation corresponds to only one Zobrist situation check code.
As a preferred solution of the present invention, score value described in step 5 is calculated by situation evaluation function.
As a preferred solution of the present invention, the truncation of Beta described in step 5 is value the worst for other side.
As a preferred solution of the present invention, the truncation of Alpha described in step 5 is the best values searched.
The invention adopts the above technical scheme compared with prior art, has following technical effect that
On the basis of Alpha-Beta searching algorithm, bound site chessboard indicates the present invention, beginning library, history lists, displacement
The thought of the technologies such as table optimization algorithm and Multiple Optimization substantially increases the efficiency that algorithm finds optimal solution；It utilizes and changes simultaneously
In generation, deepens search, so that may be by one layer of search in each layer of search to the ranking results of way to get there, and then increases greatly
The efficiency for adding beta pruning avoids the deadlock for the searching sequence for falling into the method for depending critically upon.These are improved so that improved
The shortcomings that Alpha-Beta can effectively jump out the deadlock for relying on method sequence, overcome Alpha-Beta algorithm itself, In
Good effect is achieved on intelligent Chinese chess field.
Detailed description of the invention
Fig. 1 is the flow chart of the searching method the present invention is based on Multiple Optimization, Iterative deepening beta pruning.
Fig. 2 is chessboard storage table diagram.
Fig. 3 is inventive algorithm pyramid structure figure.
Fig. 4 is state machine diagram.
Specific embodiment
Embodiments of the present invention are described below in detail, the example of the embodiment is shown in the accompanying drawings.Below by
The embodiment being described with reference to the drawings is exemplary, and for explaining only the invention, and is not construed as limiting the claims.
The prior art: we transmit two in artificial intelligence (Artificial Intelligent, AI) algorithm search
Value, first value is Alpha, that is, the best values searched, any just more useless than its smaller value, because strategy is exactly to know
The value of Alpha, any value less than or equal to Alpha will not all increase.Second value is Beta, i.e., for opponent
The worst value.It is the worst as a result, as we know that in opponent that this is that opponent can bear, he can always find one
Countermeasure is worse unlike Beta.If returning to Beta or value more better than Beta in search process, that is just enough good, moves
One side just has no chance using this strategy.
When searching for method, it is each searched for method return with Alpha and Beta related value, the pass between them
Be it is extremely important, perhaps mean search can stop and return.
If some the result of method be less than or equal to Alpha, it is exactly very poor method, therefore can be abandoned.
Because situation is with Alpha for the side to move for evaluation in this strategy.
If some method result be greater than or equal to Beta, entire node just cancels, because opponent is not intended to
Go to this situation, and it has other method can be to avoid reaching this situation.Therefore, if the evaluation that we find be greater than or
Equal to Beta, turned out this node be it is impossible, therefore it is remaining rationally method be not necessarily to search again for.
If some the result of method be greater than Alpha but be less than Beta, this method be exactly that the side that moves can examine
The way to get there of worry, unless be varied later.Therefore Alpha can be continuously increased to reflect news.It sometimes may a conjunction
Manage method and be also no more than Alpha, this be in actual combat it is recurrent, this situation is not considered at this time, thus in order to
Avoid such situation, we must upper one layer of situation of game theory select another method.
Improved technology of the present invention:
The first step, the setting of chessboard.We use position chessboard to indicate to substantially reduce AI response using bit arithmetic as basic operation
Time.
Second step, Iterative deepening search technique.If predetermined depth that we set AI is 32 steps, the search of Iterative deepening
Mode is that the depth of search is incremented to always 32 by 1 to scan for for circulation, and the benefit done so is, each
One layer of search be may be by the search of layer to the ranking results of way to get there, and then greatly increase the efficiency of beta pruning.It is searching for
During, static search is additionally used, inspection repeats the technologies such as situation and empty step cutting, so that AI thinks deeply more far-reaching
Perfect Chinese chess rule system simultaneously.Situation valuation functions calculation formula is used in the search are as follows:
Vnow=| Vblack-Vwhite|+Vaduanced
Wherein, VblackAnd VwhiteIt is the situation assessed value of red and black, PieceValue respectivelyi,jIt is position locating for chess piece
Set the sub- power value that draw in chess subtype is determined, VadvancedIt is pas score value, VncwIt is the situation scoring of current situation.
4th step, beginning library --- one stores the information bank of the preferable way to get there of corresponding situation for addition.In searching decisions tree
Before, if the way to get there that can have been had found in beginning library, can save many search times, while beginning library also guarantees
AI is provided with the style of moving of profession chess game at the very start.
5th step, use state machine --- it is scanned for according to the sequence of regulation.Execution sequence is: being in permutation table first
Search, using the way to get there of corresponding situation if having found corresponding situation.Do not have in permutation table, attempt to killer's way to get there one,
Killer's way to get there two just generates whole ways to get there to current situation if above-mentioned way to get there is all bad, selects best that in remaining way to get there
It is a.
6th step, uses permutation table.Although way to get there is different when playing chess, the situation of many ways to get there eventually led to
May be it is identical, permutation table is to have recorded these ways to get there for corresponding to situations, when having found that identical situation can be direct
Using the way to get there in permutation table.
7th step records two killer's ways to get there, is AI during searching decisions tree, relatively good in same node layer
Way to get there, since such way to get there is easier the way to get there become, so also these ways to get there are had recorded.
By the optimization of above step, best way to get there, improved Alpha-Beta pruning search algorithm flow chart can be obtained
As shown in Figure 1.
Fig. 1 describes the search routine under a certain situation of AI algorithm, i.e., current situation takes turns to AI when AI begins
Thinking processes, the end of this process represent AI and are made that decision of beginning.
This process is built on the basis of Alpha-Beta pruning search, will form decision tree, decision in search process
The current situation of the root nodes stand of tree；Root node is constantly begun by both sides, and (AI thinking is assumed to begin, and has not really fallen in chess
On disk) become new chess game state, new child node can be formed under root node by beginning for every kind；The step number begun is equivalent to this certainly
The node of the number of plies of plan tree, same layer is known as the brotgher of node.
The limit depth of AI thinking is the number of plies (final goal layer) for searching for the decision tree for reaching setting；AI algorithm uses
Iterative deepening search, and it is non-disposable search limit depth, but pass through repeatedly search, the horizontal line searched for every time and all progressively increase
One layer, therefore horizontal line (it is not final goal layer, but the destination layer of this search) represent the maximum number of plies that AI is once searched for.
The number of plies is also referred to as depth.Specific step is as follows:
Step 1, clear history table empties killer's way to get there table, empties the permutation table, (thinking of control AI of initialization timer
Time judges whether time-out in search process, omits the description of time-out judgement hereinafter).
Step 2, beginning library is searched for, beginning library is just already loaded into when user clicks and starts to play a game, this process is only
" search routine under a certain situation ", therefore do not need to search for every time and be loaded into beginning library.Beginning library includes situation check code
(the corresponding a certain unique coding of situation, no longer explain hereinafter), corresponding way to get there, way to get there score value is begun by other people games
Experience is established, and way to get there is all very outstanding, therefore the purpose for searching for beginning library is to exchange that confidence level is higher to search for the volumes of searches of very little
Hitch fruit.Small volumes of searches is because beginning library uses situation check code as HASH functional value, for the different ways to get there of same situation
It is convenient for binary search according to score value sequence, HASH search makes beginning library searching cost small plus binary search.In beginning library
It searches out as a result, then directly begin, exits search, do not search out result and then enter in next step.
Step 3, all ways to get there of current situation are obtained according to Chinese chess rule, and judge whether way to get there is unique, if unique
Terminate and move according to unique way to get there, enters in next step if not unique.
Step 4, the search number of plies is exactly the step number begun, and often falls a son and forms a new child node (new situation), away from
The child node of step number identical from root node is known as being in the brotgher of node of same layer.In decision tree, the search number of plies be will appear as
Current situation is that how many step (how many layer searched) has been walked from root node.It is iterated intensification search, if the horizontal line of search is from 1
Be incremented to 32 always, later the step of, the step in certain Iterative deepening search described, i.e., when horizontal line is x
Step.Search reaches limit depth and can stop search, and the best way to get there according to obtained in search process is begun (horizontal line, the limit
The meaning of depth sees above third section).It (is later the description that an iteration deepens search process, an iteration into next step
Deepening search is an Alpha-Beta beta pruning recursive procedure).
Step 5, permutation table is searched for, the situation check code of a certain situation is had recorded in permutation table, depth, mark (use by mark
In marking the displacement list item to be that a Beta truncation generates or an Alpha truncation generates, if the score value recorded in list item
For s, Beta cutoff flag shows that the score value of the node is at least s, and Alpha cutoff flag shows the score value of the node is at most
S), score value, best way to get there judge whether depth is consistent with current depth if finding current situation in permutation table, if one
Cause, can be directly used the way to get there in permutation table begin (not really begin, be here AI in thinking virtually beginning,
Beginning in Iterative deepening search process is all virtually to begin) new situation is formed, 8 are gone to step, is otherwise entered in next step.
Step 6, judge whether the corresponding killer's way to get there of current layer number is effective, and killer's way to get there is that Beta is generated in the brotgher of node
The way to get there of truncation, each classic two way to get there of layer become killer's way to get there and (record by the way of updating, the score value of new way to get there
Greater than killer's way to get there one, killer's way to get there one is assigned to killer's way to get there two, new way to get there is assigned to killer's way to get there one, completes primary
It updates；If new way to get there score value is less than killer's way to get there once being greater than killer's way to get there two, new way to get there is copied to killer's way to get there two, completes
It is primary to update), if killer's way to get there meets way to get there rule (i.e. killer's way to get there is effective), is begun using killer's way to get there and (virtually fallen
Son), 8 are gone to step, is otherwise entered in next step.
Step 7, all ways to get there are generated to current situation, are sorted according to the score value in history lists.The subscript of history lists is
Way to get there (way to get there is indicated by a numerical value), the value of history lists are every time plus the corresponding depth square of the way to get there.Point of history lists
Value is bigger, shows that this way to get there value is bigger.Way to get there and killer's way to get there before excluding in permutation table, according to the row in history lists
Sequence walks remaining way to get there, and every kind of way to get there will all generate a new situation and (virtually begin, from the foregoing, it will be observed that one from step 5 to step 7
Surely a son has been fallen, that is, has formd new situation, decision tree has expanded one layer, and the number of plies of search increases one layer, occurs newly
One level of child nodes), into next step.
Step 8, judge whether the depth of search reaches current iteration and deepen the horizontal line of search, if so, 9 are gone to step,
Otherwise 10 are gone to step.
Step 9, the static search of trial, i.e., with situation valuation functions, (sub- power is worth every kind of chess in table again after arrival horizontal line
The score value of sub- present position is cumulative to be obtained, and static evaluation current situation situation is used for) cooperate Alpha-Beta beta pruning to search for down
A few steps (be no more than limit depth, also may not with reach the limit of depth because may stop before reaching the limit of depth), it is therefore an objective to
To prevent the way to get there obtained in above-mentioned steps from misunderstanding situation, for example it can only search now and eat a sub way to get there, but simultaneously
Also it can choose and do not eat, the superiority and inferiority of situation after capturing is can be found that by static search.Show after static state search once repeatedly
In generation, deepens the end of search process, judges whether the limit depth reached, goes to step 4 if not reaching, into next iteration plus
Deep search；Otherwise 14 are gone to step.
Step 10, the case where judging whether to generate repetition situation, preventing from encountering " solution will also by ", causes search to be had no limits
Ground goes on, and carries out the one-sided long judgement that will sentence negative and draw in chess for program.If without situation is repeated, into next step.
Step 11, it attempts empty step to cut, is to have to find a way to get there to continue recurrence and move in the step of before, but also
It was not attempt to the mode that this step is not walked, directly opponent is allowed to begin if this step is not walked, opponent still can not knock down oneself, this
When virtually save time of search, (can say timer before) in the stipulated time makes the depth of search increase by 1
To 2 layers, into next step.
Step 12, judge whether to have killed chess, stop current iteration if having killed chess and deepen search, return to step 4, open
Beginning next iteration deepens search, otherwise enters in next step.
Step 13, the way to get there found in step 5 to step 7 is recorded in permutation table and history lists and (is had updated here
Permutation table and history lists, in search process, history lists and permutation table are exactly to generate and constantly update in this way), son is walked in exchange
Side continues current iteration and deepens the Alpha-Beta beta pruning recurrence of search to move process, i.e., attempts step 5 respectively and arrive step 7
The way to get there of generation is moved and (is virtually moved) according to the empty step cutting in step 11.First judge whether to reach pole before recurrence
Depth is limited, searching for if reaching terminates, and goes to step 14；Otherwise start recurrence, exchange walk son side (or the thinking processes of AI, and
Non- chess game really has exchanged away son side), return to step 4.
Step 14, entire search process terminates, and AI makes a move, and (this is that a step chess has really been walked on chessboard to chess, before
All it is AI virtually beginning during thinking), son side is walked in exchange, is begun to player.
Wherein, step 4 is the overall process of Iterative deepening search to step 13, and the search of each Iterative deepening is all primary
The recursive procedure of Alpha-Beta beta pruning.Step 5 is the recursive procedure of an Alpha-Beta beta pruning to step 13, and primary
Iterative deepening search process.It is known as state machine from beginning library to the sequence for generating whole this series of search plan of way to get there.
As shown in Fig. 2, being chessboard storage table diagram.Length is used to store for 256 byte arrays to entire chessboard,
Allow chessboard to get subscript of the chess piece in array rapidly by bit arithmetic, realize well chessboard operation and
The mapping relations of bit arithmetic；In addition, the type of chess piece can also be distinguished by bit arithmetic, red chess piece is 8~14, black
Chess piece is 16~22.
Regular array shows the subscript of position that every kind of chess piece can be moved in 256 arrays；Regular array is to sentence
Whether disconnected way to get there is legal, judges that other functions such as general provide more convenient operation.
Sub- power value table shows a variety of different chess pieces in the value point of different location, each chess piece has corresponding
The sub- power of 256 length is worth table, and sub- power value table plays the role of vital on estimation calculation situation score value.
Zobrist check code is the mark of current situation, often will carry out one toward one chess piece of increase on chessboard or deletion
Secondary check code calculates.Check code initial value is generated by RC4 cipher stream generator, and chess piece carries out Zobrist check code when having variation
Xor operation, so that the check code of each situation is unique.Zobrist check code is as the key assignments of permutation table and the lookup in beginning library
Foundation is made that greatest contribution to accelerate AI searching algorithm；Furthermore Zobrist check code can solve some regulatory issues, than
It such as grows, to grow to catch and sentences negative, the judgement etc. of draw in chess.
History lists stores square of search depth using way to get there as subscript, it is convenient when traversing all current ways to get there from
Optimal way to get there in history lists is begun stepping through.
Permutation table using Zobrist Key%HASH_SIZE as index value, stored in permutation table in have: depth, mark
The information such as will, score value, best way to get there.Many different ways to get there may correspond to the same final chess game situation, and permutation table is just
It is to be further simplified Alpha-Beta search process by replacing identical situation.
As shown in figure 3, AI algorithm can be described with a pyramid, the basis of algorithm is Alpha-Beta pruning search
Algorithm has been superimposed multi-level optimization technology and mode of learning on this basis.Pyramid to pushing up, embodies the execution stream of AI the bottom of by
The process of journey and AI thinking refinement.
It is the foundation of algorithm first --- Alpha-Beta pruning search algorithm, its effect are in huge decision tree
The branch for not searching for value is cut off, the workload of search is reduced.In the foundation of searching algorithm, we take a series of excellent
Change the method for search efficiency, that is, pyramidal middle section.
First layer optimizing method is Iterative deepening search technique, and Iterative deepening is exactly that search depth is stepped up to make a reservation for
Value, such as AI setting thinking stop to 32 layers, then it is not instead of directly that depth is not fixed to 32 when search, since 1
It is incremented to 32.The benefit done so be exactly search when can use it is upper one layer search as a result, increase decision tree beta pruning it is several
Rate saves computing resource.
Second layer optimizing method is beginning library, and beginning library is the way to get there for having arranged other people and having generated to the research of Chinese chess beginning
Library makes AI in the style of moving that can have profession at the very start of chess game.The realization in beginning library depends on Zobrist check code skill
Art, check code have given each situation unique coding, have facilitated the record of way to get there information, also established for the learning functionality of AI
Basis.
Third layer optimizing method is state machine, it defines the priority that AI uses left side each method, from top to bottom preferentially
Grade is successively successively decreased, and each method coordinate operation is made, and the thinking of AI also more has logic.
So far, by this series of Optimizing Search, AI mono- surely obtains a way to get there, if this way to get there is good enough,
This way to get there will be recorded in history lists.The effect of history lists is to have kept in the experience learnt in local exchange.
Iterative deepening search is exactly that the depth of search is stepped up predetermined value to save computing resource, promotes search
Accuracy and efficiency.In Iterative deepening search technique, static search and empty step tailoring technique have been used mainly to overcome level
Line effect, so that further being extended in the search of certain situations.
Static state search uses situation estimation function, is similar to Alpha-Beta searching algorithm, and according to MVV/LVA score value pair
The algorithm of capturing searched is ranked up, and MVV/LVA is upper very simple the method sequence skill of an application, to search at first
The best method of capturing of rope.This technology assumes that best capturing is to have maximum son.If more than one chess piece can be had
Maximum son, it is assumed that it is best for going to eat with the smallest son.
Sky step is cut with the venture strategy that may ignore important route, so that branch factor when Chinese chess is searched for falls sharply,
It leads to significantly improving for search depth, because in most cases it significantly reduces the quantity of search.Its working principle
Be cut a large amount of useless methods and only retain method.
The some relatively good ways to get there for corresponding to situation when chess game just starts are stored in beginning library, make calling program in chess game
Flexible and changeable reliable way to get there can also be provided quickly without Alpha-Beta search when just starting, and this dramatically reduces search
Time and complexity.
Killer's way to get there is exactly that the way to get there of Beta truncation is generated in the brotgher of node, in Alpha-Beta search process, will
To best way to get there and time method convenient for walking store as killer's way to get there, since killer's way to get there is often easier generation Beta
The way to get there of truncation, thus killer's way to get there can also substantially reduce the complexity of search.
As shown in figure 4, one state machine of construction, to describe several stages of way to get there sequence, search order is suitable as illustrated
Sequence optimizes, i.e., first searches for the way to get there in beginning library, search again for the way to get there in permutation table, then attempt to two killer's ways to get there,
The mode for generating all ways to get there only can be just taken to be selected when the search of above-mentioned way to get there is all come to nothing according to history lists
Select best way to get there.
During playing a game, the situation lived through can be recorded in permutation table, and outstanding way to get there can be recorded to history
In table.
The way to get there that can generate Beta truncation is had recorded in history lists or cannot generate Beta truncation, but it is that all PV are walked
Best way to get there in method (vl > vlAlpha).History lists is defined as nHistoryTable [mv] +=nDepth* in a program
nDepth；If it find that the way to get there in history lists can be truncated, because of this recorded in history lists when AI algorithm search
Way to get there can save well a large amount of search time enough.
Permutation table has recorded depth, mark, score value and the best way to get there of various situations, base according to Zobrist check code
This thought is different way to get there and is likely to be obtained identical situation, by the displacement to identical situation, can reduce Alpha-Beta
The complexity of search improves search efficiency.
The above examples only illustrate the technical idea of the present invention, and this does not limit the scope of protection of the present invention, all
According to the technical idea provided by the invention, any changes made on the basis of the technical scheme each falls within the scope of the present invention
Within.
Claims (5)
1. searching method of the Chinese chess based on Multiple Optimization, Iterative deepening beta pruning, which comprises the steps of:
Step 1, for current situation, permutation table, killer's way to get there table and history lists are emptied, initialization controls the meter for time of beginning
When device；
Step 2, search, which is played a game, starts the preceding beginning library being arranged, if searching the correspondence way to get there of current situation in beginning library,
Begun according to search result, and terminates to search for；Otherwise enter in next step；
Step 3, all ways to get there of current situation are obtained according to Chinese chess rule, and whether judge way to get there unique, if uniquely, according to
Unique way to get there is begun, and terminates to search for；Otherwise the horizontal line of setting Iterative deepening searchi=1, limit depth 32 enters
In next step；
Step 4, from depth 1 to depthiIt is iterated intensification search；
Situation check code, the depth, mark, score value, way to get there of the Beta truncation or Alpha truncation that are encountered when step 5, by search
It is recorded in permutation table；Search for permutation table, if finding current situation in permutation table, judge depth whether with current depth one
It causes, if unanimously, virtually beginning to form new situation using the way to get there in permutation table, going to step 8, otherwise enters in next step；
Step 6, two ways to get there classic in the corresponding all ways to get there of current depth as killer's way to get there and are recorded in killer
In way to get there table, judge whether the corresponding killer's way to get there of current depth is effective, virtually begun if effectively using killer's way to get there, turns step
Rapid 8, otherwise enter in next step；
Step 7, all ways to get there are generated to current situation, way to get there and killer's way to get there in permutation table are excluded, according to history lists
In score value remaining way to get there is ranked up, according to sequence first way to get there virtually begun, into next step；
Step 8, judge whether the depth of search reaches the horizontal line that current iteration deepens searchi, if so, going to step 9, otherwise
Go to step 10；
Step 9, when the depth of Iterative deepening search reaches horizontal lineiWhen, static search is carried out, after static state search, is sentenced
It cuts off the water supply horizontal lineiWhether depth is reached the limit of, if then going to step 14, otherwise,i=i+ 1, and return step 4；
Step 10, judge whether to generate and repeat situation, if repeating situation, then continue to determine whether that one-sided length, which will occur, to be sentenced
The case where negative or draw in chess, stops current iteration if occurring and deepens search,i=i+ 1, and return step 4；If not occurring into
Enter in next step；If not repeating situation, also enter in next step；
Step 11, it attempts empty step to cut, and enters in next step；
Step 12, judge whether to have killed chess, stop current iteration if having killed chess and deepen search,i=i+ 1, return step 4,
Start next iteration and deepen search, otherwise enters in next step；
Step 13, the way to get there found in step 5 to step 7 is recorded in permutation table and history lists, determined level lineiWhether arrive
Up to limit depth, searching for if reaching terminates, and goes to step 14；Otherwisei=i+ 1, what trial step 5 was found into step 7 respectively walks
The method empty step in step 11 of virtually beginning or execute is cut, and virtual switch walks son side, and progress recurrence is moved, return step 4；
Step 14, entire search process terminates, and begins by final search result, and exchanges son side, begins to player.
2. searching method of the Chinese chess based on Multiple Optimization, Iterative deepening beta pruning, feature exist according to claim 1
In beginning library described in step 2 is made of Zobrist situation check code, way to get there description, way to get there score value, and a situation is corresponding only
A Zobrist situation check code one by one.
3. searching method of the Chinese chess based on Multiple Optimization, Iterative deepening beta pruning, feature exist according to claim 1
In score value described in step 5 is calculated by situation evaluation function.
4. searching method of the Chinese chess based on Multiple Optimization, Iterative deepening beta pruning, feature exist according to claim 1
In the truncation of Beta described in step 5 is value the worst for other side.
5. searching method of the Chinese chess based on Multiple Optimization, Iterative deepening beta pruning, feature exist according to claim 1
In the truncation of Alpha described in step 5 is the best values searched.
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