The History Heuristic and Alpha-Beta Search Enhancements in Practice
IEEE Transactions on Pattern Analysis and Machine Intelligence
New advances in Alpha-Beta searching
CSC '96 Proceedings of the 1996 ACM 24th annual conference on Computer science
Computer chess move-ordering schemes using move influence
Artificial Intelligence
KnightCap: A Chess Programm That Learns by Combining TD(lambda) with Game-Tree Search
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Combining online and offline knowledge in UCT
Proceedings of the 24th international conference on Machine learning
BayesChess: A computer chess program based on Bayesian networks
Pattern Recognition Letters
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This paper proposes a new mechanism for pruning a search game tree in computer chess. The algorithm stores and then reuses chains or sequences of moves, built up fromprevious searches. These move sequences have a built-in forward-pruning mechanism that can radically reduce the search space. A typical search process might retrieve a move from a Transposition Table, where the decision of what move to retrieve would be based on the position itself. This algorithm stores move sequences based on what previous sequences were better, or caused cutoffs. The sequence is then returned based on the current move only. This is therefore position independent and could also be useful in games with imperfect information or uncertainty, where the whole situation is not known at any one time. Over a small set of tests, the algorithm was shown to clearly out perform Transposition Tables, both in terms of search reduction and game-play results. Finally, a completely new search process will be suggested for computer chess or games in general.