Monte-carlo go developments
Bruno Bouzy,Bernard Helmstetter +1 more
- pp 159-174
TLDR
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.Abstract:
We describe two Go programs, Olga and Oleg, developed by a Monte-Carlo approach that is simpler than Bruegmann’s (1993) approach. Our method is based on Abramson (1990). We performed experiments, to assess ideas on (1) progressive pruning, (2) all moves as first heuristic, (3) temperature, (4) simulated annealing, and (5) depth-two tree search within the Monte-Carlo framework. Progressive pruning and the all moves as first heuristic are good speed-up enhancements that do not deteriorate the level of the program too much. Then, using a constant temperature is an adequate and simple heuristic that is about as good as simulated annealing. The depth-two heuristic gives deceptive results at the moment. The results of our Monte-Carlo programs against knowledge-based programs on 9x9 boards are promising. Finally, the ever-increasing power of computers lead us to think that Monte-Carlo approaches are worth considering for computer Go in the future.read more
Citations
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Journal ArticleDOI
Mastering the game of Go with deep neural networks and tree search
David Silver,Aja Huang,Chris J. Maddison,Arthur Guez,Laurent Sifre,George van den Driessche,Julian Schrittwieser,Ioannis Antonoglou,Veda Panneershelvam,Marc Lanctot,Sander Dieleman,Dominik Grewe,John Nham,Nal Kalchbrenner,Ilya Sutskever,Timothy P. Lillicrap,Madeleine Leach,Koray Kavukcuoglu,Thore Graepel,Demis Hassabis +19 more
TL;DR: Using this search algorithm, the program AlphaGo achieved a 99.8% winning rate against other Go programs, and defeated the human European Go champion by 5 games to 0.5, the first time that a computer program has defeated a human professional player in the full-sized game of Go.
Book ChapterDOI
Bandit based monte-carlo planning
Levente Kocsis,Csaba Szepesvári +1 more
TL;DR: In this article, a bandit-based Monte-Carlo planning algorithm is proposed for large state-space Markovian decision problems (MDPs), which is one of the few viable approaches to find near-optimal solutions.
Book ChapterDOI
Efficient selectivity and backup operators in Monte-Carlo tree search
TL;DR: A new framework to combine tree search with Monte-Carlo evaluation, that does not separate between a min-max phase and a Monte- carlo phase is presented, that provides finegrained control of the tree growth, at the level of individual simulations, and allows efficient selectivity.
BookDOI
Artificial Intelligence for Games
Ian Millington,John Funge +1 more
TL;DR: Artificial Neural Networks Board Games Game Theory Minimaxing Transposition Tables and Memory Memory-Enhanced Test Algorithms Opening Books and Other Set Plays Further Optimizations Turn-Based Strategy Games Supporting Technologies Execution Management Scheduling Anytime Algorithm Level of Detail World Interfacing Communication Getting Knowledge Efficiently Event Managers Polling Stations Sense Management Tools and Content Creation.
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.
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