'''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. The linear regression itself is learned through previous tree searches, making the heuristic a kind of dynamic search control.<ref name="Bulitko2008">{{cite journal |last1=Bulitko |first1=Vadim |last2=Lustrek |first2=Mitja |last3=Schaeffer |first3=Jonathan |last4=Bjornsson |first4=Yngvi |last5=Sigmundarson |first5=Sverrir |title=Dynamic control in real-time heuristic search |journal=Journal of Artificial Intelligence Research |date=1 June 2008 |volume=32 |pages=419-452 |url=https://dl.acm.org/doi/abs/10.5555/1622673.1622683 |language=EN}}</ref> 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>
