Board Games AI

Board Games AI

DOI: 10.4018/978-1-5225-7368-5.ch006
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The classical area of AI application is the board game. This chapter introduces the two most prominent AI approaches used in developing board game agents—the MinMax algorithm and machine learning—and explains their usage in playing games like Tic-Tac-Toe, Checkers, Othello, Chess, Go, etc. against human opponents. The game tree is essentially a directed graph, where the nodes represent the positions in the game and the edges the moves. Even a simple board game like Tic-Tac Toe (naughts and crosses) has as many as 255,168 leaf nodes in the game tree. Traversing the complete game tree becomes an NP-hard problem. Alpha-beta pruning is used to estimate the short-cuts through the game tree. The board game strategy depends on the evaluation function, which is a heuristic indicating how good the player's current move is in winning the game. Machine learning algorithms try to evolve or learn the agent's game playing strategy based on the evaluation function.
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Playing games intelligently and trying to win against human champions has been a grand challenge for Artificial Intelligence from its inception. Chess, in particular, has been referred to as the Drosophila of AI. The first study in computer chess was published by Claude Shannon in 1950 (Shanon, 1950). However, the first working AI programs were for playing checkers (Samuel, 1960; Samuel, 1967). In the 1970s and 1980s, computer-games research concentrated on chess and the brute-force search approach (Schaeffer, 2002). The triumph of AI in computer games came in the 1990s when to everyone’s surprise AI programs began to defeat the reigning world champions. In 1994, the program CHINOOK won the World Man–Machine Championship (Schaeffer, 1997). Three year later, IBM’S DEEP BLUE defeated the World Chess Champion Garry Kasparov (Hsu, 2002) and LOGISTELLO won against the Othello Champion Takeshi Murakami (Buro, 1997). Finally, in 2011, IBM’s WATSON defeated the world champions in the quiz game of Jeopardy.

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