Progressive Strategies for Monte-Carlo Tree Search

Guillaume M. J-b. Chaslot, Mark H. M. Winands, H. Jaap van den Herik, Jos W. H. M. Uiterwijk, Bruno Bouzy

Research output: Contribution to journalArticleAcademicpeer-review


Monte-Carlo Tree Search (MCTS) is a new best-first search guided by the results of Monte-Carlo simulations. In this article, we introduce two progressive strategies for MCTS, called progressive bias and progressive unpruning. They enable the use of relatively time-expensive heuristic knowledge without speed reduction. Progressive bias directs the search according to heuristic knowledge. Progressive unpruning first reduces the branching factor, and then increases it gradually again. Experiments assess that the two progressive strategies significantly improve the level of our Go program Mango. Moreover, we see that the combination of both strategies performs even better on larger board sizes.
Original languageEnglish
Pages (from-to)343-357
JournalNew Mathematics and Natural Computation
Issue number3
Publication statusPublished - 1 Nov 2008


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