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5 Conclusion Our brief historical overview of search paradigms in computer chess argued that the pure brute-force searchers lost their traditional dominance of the field around 1990. Since then selective forward-pruning techniques have again taken the lead, guided by the spectacular achievements of the null move in microcomputer practice. Today, nearly all world-class chess programs apply various clever forwardpruning schemes with good success. This chapter adds adaptive null-move pruning to the collection of successful forward-pruning methods in computer chess.
In general, forward-pruning decisions are rather based on fallible educated guesses than on infallible backward propagation of terminal scores. 3 Advanced Search Techniques 25 continuations as compared with a pure full-width search. Consequently, a forwardpruning search may sometimes miss good continuations which backward pruning discovers easily. Forward pruning only bets that it is safe to cut off - backward pruning, however, really knows so. The secret of successful forward pruning is to reduce the selective risks to an acceptable level while at the same time preserving the performance gains.
The prerequisite for their practical usefulness is to preserve the tactical strength of the search. Successful forward-pruning techniques fulfill this basic requirement while scaling well with increasing search depth. Null-move pruning is a dynamic forward-pruning scheme that generates selective cutoffs at nominal full-width nodes where the side-to-move is not in check [21, 64, 80]. Even the recursive application of null-move pruning along a single search path exhibits only minor tactical weaknesses while reducing the overall search effort substantially.