The development of computer-based algorithms based on gobang game

Abstract

Gobang is a worldwide two-player strategy board game, which is popular especially in the Asian region. Recently, the science and technology has been developing fast and artificial intelligence was applied in numerous fields like the board game. Gobang, a strategy game with moderate difficulty, is a suitable example for people to test algorithms and solve board game problems. As a result, many related algorithms were appearing with different advantages and disadvantages. In this work, these already existed algorithms, game tree, minimax search, alpha-beta pruning, genetic algorithm and monte carlo tree search, were discussed and compared. The results and comparison showed that game tree and minimax search had a large number of nodes to calculate to reach a suitable search depth, about 1.00E+12 and 1.29E+14 respectively, which meant they need a long calculating time, while the alpha-beta pruning need to calculate about 2.2E+07 nodes and genetic algorithm only need to calculate about 1.00E+04 nodes, which cost 0.6 seconds for every move. Plus, the monte carlo tree search could reach nearly 100% win rate through self-play, which making gobang algorithm become more refined. Additionally, these algorithms had already made gobang AI powerful with fast move and high win rate, so they also had been applied in many different fields to develop and spread gobang.