A Study On Gobang Algorithm Based On Computer Game Theory

Artificial intelligence is the hottest topics in recent years,a variety of artificial intelligence products after another.As its branch of the machine game alsohas very much attention.In recent years,the country has always attached great importance to this area,and to support the promotion of a lot of computer games.Game machines by simulating human intelligence to solve practical problems.This is also the practical value of the study.This design uses Gobang for the study of existing research,a comprehensive international forefront of research trends,the mainly game process of the search algorithm research design.The following is the main content of the design work.First,to study and learn the mainly algorithm of computer game.Thento analysis the chess rules and the development of Gobang.Design the overall system framework and chessboard generation,and display go chess,time,such as the interface design.Secondly,according to the rules of Gobang,this paper proposes a game tree search algorithm and implementation.Based on this algorithm improvement.By adding the iterative deepening and window searching method in the Alpha-Beta pruning algorithm,the initial program is greatly improved.The evaluation function also plays an important role in the whole system,and the evaluation function is researched and designed based on this design.Finally,in order to improve the chess performance greatly,a method of using machine learning instead of the search algorithm is put forward to solve the problem of unsatisfactory search results.In this paper,a program environment is designed which can carry on playing chess independently.And it completes the game process independently.Then,experimental results show that chess has greatly improvedwith a certain significance and use of research value.