Adaptive Null-Move Pruning

By E. A. Heinz. 1999

How researchers see this paper

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Giraffe: Using Deep Reinforcement Learning to Play Chess (2015, background): “It is already well-established that strategic extensions, reductions, and pruning have huge beneﬁts on the performance of chess engines [25] [26], however, existing heuristics for making those decisions are mechanical and quite crude, and must be done very conservatively to not miss tactical…”
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).”
Efficiency of three forward-pruning techniques in shogi: Futility pruning, null-move pruning, and Late Move Reduction (LMR) (2012, background): “One improvement from the plain sample code is to vary R with depth [11], where the value of R changes from the conservative value 2 to the aggressive value 3 when the depth is larger than a certain threshold.”