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Multi-Prob Cut is a heuristic used in alpha–beta pruning search.[1] The Prob Cut heuristic estimates evaluation scores at deeper levels of the search tree using a linear regression between deeper and shallower scores. Min Prob Cut extends this approach to multiple levels of the search tree. The linear regression itself is learned through previous tree searches, making the heuristic a kind of dynamic search control.[2] It is particularly useful in games such as Othello where there is a a strong correlation between evaluations scores at deeper and shallower levels.[3][4]
References
- ^ Buro, Michael (1997). "Experiments with Multi-ProbCut and a New High-Quality Evaluation Function for Othello". Games in AI Research: 77–96.
- ^ Bulitko, Vadim; Lustrek, Mitja; Schaeffer, Jonathan; Bjornsson, Yngvi; Sigmundarson, Sverrir (1 June 2008). "Dynamic control in real-time heuristic search". Journal of Artificial Intelligence Research. 32: 419–452.
- ^ Fürnkranz, Johannes (2001). Machines that learn to play games | Guide books. Nova Science Publishers, Inc.6080 Jericho Tpke. Suite 207 Commack, NYUnited States: Nova Science Publishers, Inc. pp. 11–59. ISBN 978-1-59033-021-0.
{{cite book}}: CS1 maint: ___location (link) - ^ Heinz, Ernst A. (2013). Scalable Search in Computer Chess: Algorithmic Enhancements and Experiments at High Search Depths. Springer Science & Business Media. p. 32. ISBN 978-3-322-90178-1.
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