Heuristics: intelligent search strategies for computer problem solving
Heuristics: intelligent search strategies for computer problem solving
Searching with probabilities
Low overhead alternatives to SSS*
Artificial Intelligence
Game tree searching by min/max approximation
Artificial Intelligence
Conspiracy numbers for min-max search
Artificial Intelligence
Artificial Intelligence - Special issue on computer chess
The Secret of Selective Game Tree Search, When Using Random-Error Evaluations
STACS '02 Proceedings of the 19th Annual Symposium on Theoretical Aspects of Computer Science
Parallel Controlled Conspiracy Number Search
Euro-Par '02 Proceedings of the 8th International Euro-Par Conference on Parallel Processing
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When playing board games like chess, checkers, othello etc., computers use game tree search algorithms to evaluate a position. The greatest success of game tree search so far, has been the victory of the chess machine 'Deep Blue' vs. G. Kasparov, the best human chess player in the world. When a game tree is too large to be examined exhaustively, the standard method for computers to play games is as follows. A partial game tree (envelope) is chosen for examination. This partial game tree may be any subtree of the complete game tree, rooted at the starting position. It is explored by the help of the αβ-algorithm, or any of its variants. All αβ-variants have in common that a single faulty leaf evaluation may cause a wrong decision at the root. To overcome this insecurity, we propose Cc2s, a new algorithm, which selects an envelope in a way that the decision at the root is stable against a single faulty evaluation. At the same time, it examines this envelope efficiently. We describe the algorithm and analyze its time behavior and correctness. Moreover, we are presenting some experimental results from the domain of chess. Cc2s is used in the parallel chess program P.ConNerS, which won the 8th International Paderborn Computer Chess Championship 1999.