The History Heuristic and Alpha-Beta Search Enhancements in Practice
IEEE Transactions on Pattern Analysis and Machine Intelligence
Singular extensions: adding selectivity to brute-force searching
Artificial Intelligence - Special issue on computer chess
A statistical study of selective min-max search in computer chess
A statistical study of selective min-max search in computer chess
CG '08 Proceedings of the 6th international conference on Computers and Games
Expert-driven genetic algorithms for simulating evaluation functions
Genetic Programming and Evolvable Machines
The relative history heuristic
CG'04 Proceedings of the 4th international conference on Computers and Games
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The efficiency of the αβ-algorithm as a minimax search procedure can be attributed to its effective pruning at so called cut-nodes; ideally only one move is examined there to establish the minimax value. This paper explores the benefits of investing additional search effort at cut-nodes by expanding other move alternatives as well. Our results show a strong correlation between the number of promising move alternatives at cut-nodes and a new principal variation emerging. Furthermore, a new forward pruning method is introduced that uses this additional information to ignore potentially futile subtrees. We also provide experimental results with the new pruning method in the domain of chess.