Summary
The new forward-pruning techniques of adaptive null-move pruning (A), extended futility pruning (E), and limited razoring (L) from the preceding chapters differ substantially in nature. Hence, it is unclear how well they combine with each other and which level of effectiveness their combination achieves. Based on the names of the three building blocks, we call the combined scheme AEL pruning. This chapter adds AEL pruning to the collection of practically successful forward-pruning methods in computer chess.
AEL pruning is easy and efficient to implement. Extensive experiments with all 2180 test positions from the well-known tactical test suites ECM, WAC, and WCS show that it fully preserves the tactical strength of DarkThought while reducing its search effort by 20%–50% on average at nominally fixed search depths of 8–12 plies. The reduction in search effort scales as well with additional search depth as that for any of the three combined methods alone. The positive results of 580 test games (self-play and versus other strong chess programs) provide further empirical evidence for the practical usefulness of AEL pruning.
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© 2000 Springer Fachmedien Wiesbaden
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Heinz, E.A. (2000). AEL Pruning. In: Scalable Search in Computer Chess. Computational Intelligence. Vieweg+Teubner Verlag, Wiesbaden. https://doi.org/10.1007/978-3-322-90178-1_5
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DOI: https://doi.org/10.1007/978-3-322-90178-1_5
Publisher Name: Vieweg+Teubner Verlag, Wiesbaden
Print ISBN: 978-3-528-05732-9
Online ISBN: 978-3-322-90178-1
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