O
Olivier Teytaud
Researcher at Facebook
Publications - 204
Citations - 3801
Olivier Teytaud is an academic researcher from Facebook. The author has contributed to research in topics: Evolutionary algorithm & Monte Carlo tree search. The author has an hindex of 31, co-authored 197 publications receiving 3525 citations. Previous affiliations of Olivier Teytaud include Google & University of Paris-Sud.
Papers
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of UCT with Patterns in Monte-Carlo Go
TL;DR: MoGo as mentioned in this paper is the first computer Go program using UCB1 for multi-armed bandit problem and also the intelligent random simulation with patterns which has improved significantly the performance of MoGo, which is now a top level Go program on $9\times9$ and $13\times13$ Go boards.
Modification of UCT with Patterns in Monte-Carlo Go
TL;DR: A Monte-Carlo Go program, MoGo, which is the first computer Go program using UCT, is developed, and the modification of UCT for Go application is explained and also the intelligent random simulation with patterns which has improved significantly the performance of MoGo.
Journal ArticleDOI
The grand challenge of computer Go: Monte Carlo tree search and extensions
Sylvain Gelly,Levente Kocsis,Marc Schoenauer,Michèle Sebag,David Silver,Csaba Szepesvári,Olivier Teytaud +6 more
TL;DR: This paper describes the leading algorithms for Monte-Carlo tree search and explains how they have advanced the state of the art in computer Go.
Book ChapterDOI
Continuous upper confidence trees
TL;DR: It is guess that the double-progressive widening trick can be used for other algorithms as well, as a general tool for ensuring a good bias/variance compromise in search algorithms.
Journal ArticleDOI
The Computational Intelligence of MoGo Revealed in Taiwan's Computer Go Tournaments
Chang-Shing Lee,Mei-Hui Wang,Guillaume Chaslot,Jean-Baptiste Hoock,Arpad Rimmel,Olivier Teytaud,Shang-Rong Tsai,Shun-Chin Hsu,Tzung-Pei Hong +8 more
TL;DR: The results reveal that MoGo can reach the level of 3 Dan (3D) with: (1) good skills for fights, (2) weaknesses in corners, in particular, for "semeai" situations, and (3) strengths in favorable situations such as handicap games.