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In game theory, Multi-ProbCut is a heuristic used in alpha–beta pruning of the search tree.[1] The ProbCut heuristic estimates evaluation scores at deeper levels of the search tree using a linear regression between deeper and shallower scores. Multi-ProbCut 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 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. 34 (4): 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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