'''Multi-Prob Cut''' is a heuristic used in [[alpha–beta pruning]] search.<ref name="Buro1997">{{cite journal |last1=Buro |first1=Michael |title=Experiments with Multi-ProbCut and a New High-Quality Evaluation Function for Othello |journal=Games in AI Research |date=1997 |pages=77-96 |url=http://citeseerx.ist.psu.edu/viewdoc/versions?doi=10.1.1.19.1136 |language=en}}</ref> 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. It is particularly useful in games such as [[Othello]] where there is a a strong correlation between evaluations scores at deeper and shallower levels.<ref name="Fürnkranz2001">{{cite book |last1=Fürnkranz |first1=Johannes |title=Machines that learn to play games {{!}} Guide books |date=2001 |publisher=Nova Science Publishers, Inc. |___location=Nova Science Publishers, Inc.6080 Jericho Tpke. Suite 207 Commack, NYUnited States |isbn=978-1-59033-021-0 |pages=11-59 |url=https://dl.acm.org/doi/book/10.5555/644391}}</ref><ref name="Heinz2013">{{cite book |last1=Heinz |first1=Ernst A. |title=Scalable Search in Computer Chess: Algorithmic Enhancements and Experiments at High Search Depths |date=2013 |publisher=Springer Science & Business Media |isbn=978-3-322-90178-1 |page=32 |url=https://books.google.com/books?id=KkQBCAAAQBAJ&lr=&source=gbs_navlinks_s |language=en}}</ref>
