Fail-High Reductions

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Fail-high reductions (FHR), as proposed and examined by Rainer Feldmann, and implemented in the program Zugzwang, are based on the Null Move Observation (NMO) - similar to the Null Move Heuristic (NMH). The idea is to search to shallower depth at positions that are seemingly quiet and where the side to move has established a substantial advantage, according to a static evaluation. Apart from the evaluation function, the heuristic requires an additional function that returns a value indicating threats against the side to move and therefor the quietness of a position.

=Implementation= FHR is applied at expected Fail-high or Cut-Nodes recursively inside a NegaScout-framework. Feldmann's implementation did no re-search with the original depth, if the shallow search didn't confirm the fail high. While NMH relies on a dynamic search, but cuts the whole subtree - FHR uses a static threat detection. FHR is not as vulnerable as NMH in situations, where the NMO fails - namely in zugzwang. eval, threat := evaluate(...); if ( eval - threat >= beta && alpha == beta - 1) reduce depth by one

=See also=
 * Double Null Move
 * Late Move Reductions
 * Multi-Cut
 * Null Move Pruning
 * Null Move Reductions
 * ProbCut
 * Zugzwang
 * Zugzwang (Program)

=Publications=
 * Rainer Feldmann (1997). Fail-High Reductions. Advances in Computer Chess 8, available as pdf from CiteSeerX
 * Rainer Feldmann, Burkhard Monien (1998). Selective Game Tree Search on a Cray T3E. ps
 * Yngvi Björnsson, Tony Marsland (2000). Selective Depth-First Search Methods. Games in AI Research (eds. Jaap van den Herik and Hiroyuki Iida), pp. 31-45. Universiteit Maastricht, Maastricht, The Netherlands. ISBN 90-621-6416-1. pdf preprint

=Forum Posts=
 * Fail High reductions by Jon Dart, rgcc, October 7, 1996
 * Fail high reductions by Russell Reagan, CCC, July 01, 2003
 * Fail High Reductions - question about researches by milix, CCC, November 11, 2004

=References=

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