Artificial Intelligence - Chips challenging champions: games, computers and Artificial Intelligence
Computers, Chess and Long-Range Planning
Computers, Chess and Long-Range Planning
Long-range planning in computer chess
ACM '83 Proceedings of the 1983 annual conference on Computers : Extending the human resource
Bandit based monte-carlo planning
ECML'06 Proceedings of the 17th European conference on Machine Learning
Building a world-champion arimaa program
CG'04 Proceedings of the 4th international conference on Computers and Games
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In this paper we present our ideas for an Arimaa-playing program (also called a bot) that uses plans and pattern matching to guide a highly selective search. We restrict move generation to moves in certain move categories to reduce the number of moves considered by the bot significantly. Arimaa is a modern board game that can be played with a standard Chess set. However, the rules of the game are not at all like those of Chess. Furthermore, Arimaa was designed to be as simple and intuitive as possible for humans, yet challenging for computers. While all established Arimaa bots use alpha-beta search with a variety of pruning techniques and other heuristics ending in an extensive positional leaf node evaluation, our new bot, Rat, starts with a positional evaluation of the current position. Based on features found in the current position – supported by pattern matching using a directed position graph – our bot Rat decides which of a given set of plans to follow. The plan then dictates what types of moves can be chosen. This is another major difference from bots that generate “all” possible moves for a particular position. Rat is only allowed to generate moves that belong to certain categories. Leaf nodes are evaluated only by a straightforward material evaluation to help avoid moves that lose material. This highly selective search looks, on average, at only 5 moves out of 5,000 to over 40,000 possible moves in a middle game position.