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Progressive Strategies For Monte-Carlo Tree Search

  • GUILLAUME M. J-B. CHASLOT

    ()

    (MICC-IKAT, Games and AI Group, Faculty of Humanities and Sciences, Universiteit Maastricht, P.O. Box 616, 6200 MD Maastricht, The Netherlands)

  • MARK H. M. WINANDS

    ()

    (MICC-IKAT, Games and AI Group, Faculty of Humanities and Sciences, Universiteit Maastricht, P.O. Box 616, 6200 MD Maastricht, The Netherlands)

  • H. JAAP VAN DEN HERIK

    ()

    (MICC-IKAT, Games and AI Group, Faculty of Humanities and Sciences, Universiteit Maastricht, P.O. Box 616, 6200 MD Maastricht, The Netherlands)

  • JOS W. H. M. UITERWIJK

    ()

    (MICC-IKAT, Games and AI Group, Faculty of Humanities and Sciences, Universiteit Maastricht, P.O. Box 616, 6200 MD Maastricht, The Netherlands)

  • BRUNO BOUZY

    ()

    (Centre de Recherche en Informatique de Paris 5, Université Paris 5 Descartes, 45, rue des Saints Pères, 75270 Cedex 06, France)

Registered author(s):

    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.

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    Article provided by World Scientific Publishing Co. Pte. Ltd. in its journal New Mathematics and Natural Computation.

    Volume (Year): 04 (2008)
    Issue (Month): 03 ()
    Pages: 343-357

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    Handle: RePEc:wsi:nmncxx:v:04:y:2008:i:03:p:343-357
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