Extended Futility Pruning

By E. A. Heinz. 1998

How researchers see this paper

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Giraffe: Using Deep Reinforcement Learning to Play Chess (2015, background): “While modern chess engines have improved on that by introducing various types of extensions and reductions [36] [37] [38], the fundamental idea of using depth to deﬁne search trees has not changed.”
Large-Scale Optimization for Evaluation Functions with Minimax Search (2014, methodology): “…& Norvig, 2002), static exchange evaluation (Reul, 2010), killer and history heuristics (Akl & Newborn, 1977; Schaeffer, 1989), null move pruning (Adelson-Velskiy, Arlazarov, & Donskoy, 1975; Heinz, 1999), futility pruning (Schaeffer, 1986; Heinz, 1998), and late move reductions (Romstad, 2010).”
Genetic algorithms for mentor-assisted evaluation function optimization (2008, background): “…(history + killer heuristic) [1, 13, 23, 24], multi-cut pruning [7, 8], selective extensions [3, 6] (consisting of check, one-reply, mate-threat, recapture, and passed pawn extensions), transposition table [21, 27], futility pruning near leaf nodes [17], and blockage detection in endgames [11].”
Efficiency of three forward-pruning techniques in shogi: Futility pruning, null-move pruning, and Late Move Reduction (LMR) (2012, methodology): “This pruning technique is called futility pruning [8,9], and a sample code of the technique is shown in between line 7 and 10 of Fig.”