Multi-cut &agr;&bgr;-pruning in game-tree search
Theoretical Computer Science
Computer Chess
IBM's Deep Blue Chess Grandmaster Chips
IEEE Micro
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Chess as problem solving: the development of a tactics analyzer.
Chess as problem solving: the development of a tactics analyzer.
Blockage detection in pawn endings
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In this paper we review the conventional versions of null-move pruning, and present our enhancements which allow for a deeper search with greater accuracy. While the conventional versions of null-move pruning use reduction values of R≤ 3, we use an aggressive reduction value of R= 4 within a verified adaptive configuration which maximizes the benefit from the more aggressive pruning, while limiting its tactical liabilities. Our experimental results using our grandmaster-level chess program, Falcon, show that our null-move reductions(NMR) outperform the conventional methods, with the tactical benefits of the deeper search dominating the deficiencies. Moreover, unlike standard null-move pruning, which fails badly in zugzwang positions, NMR is impervious to zugzwangs. Finally, the implementation of NMR in any program already using null-move pruning requires a modification of only a few lines of code.