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Bruno Bouzy
Researcher at Paris Descartes University
Publications - 46
Citations - 1485
Bruno Bouzy is an academic researcher from Paris Descartes University. The author has contributed to research in topics: Computer Go & Monte Carlo method. The author has an hindex of 15, co-authored 46 publications receiving 1382 citations. Previous affiliations of Bruno Bouzy include University of Paris.
Papers
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Journal ArticleDOI
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 +4 more
TL;DR: Two progressive strategies for MCTS are introduced, called progressive bias and progressive unpruning, which enable the use of relatively time-expensive heuristic knowledge without speed reduction.
Journal ArticleDOI
Computer Go: an AI oriented survey
Bruno Bouzy,Tristan Cazenave +1 more
TL;DR: The goal of this paper is to present Computer Go by showing the links between existing studies on Computer Go and different AI related domains: evaluation function, heuristic search, machine learning, automatic knowledge generation, mathematical morphology and cognitive science.
Book ChapterDOI
Monte-carlo go developments
Bruno Bouzy,Bernard Helmstetter +1 more
TL;DR: Two Go programs are described, Olga and Oleg, developed by a Monte-Carlo approach that is simpler than Bruegmann’s (1993) approach, and the ever-increasing power of computers lead us to think that Monte- carlo approaches are worth considering for computer Go in the future.
Proceedings Article
Monte-Carlo strategies for computer Go
TL;DR: Objective Monte-Carlo is a move-selection strategy that adjusts the amount of exploration and exploitation automatically and outperforms the two classical strategies previously proposed for Monte- carlo Go: Simulated Annealing and Progressive Pruning.
Journal ArticleDOI
Associating domain-dependent knowledge and Monte Carlo approaches within a Go program
TL;DR: This paper underlines the association of two computer go approaches, a domain-dependent knowledge approach and Monte Carlo and sets up experiments demonstrating the relevance of this association, used by Indigo at the 8th computer olympiad.