Monte-Carlo Go Developments

Bouzy, Bruno; Helmstetter, Bernard

doi:10.1007/978-0-387-35706-5_11

Cited by 78 publications

(53 citation statements)

References 13 publications

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“…Monte-Carlo evaluation consists in averaging the outcome of several continuations. It is an usual technique in games with randomness or partial observability [5,23,26,14,17], but can also be applied to deterministic games, by choosing actions at random until a terminal state is reached [1,9,10].…”

Section: Introductionmentioning

confidence: 99%

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Efficient Selectivity and Backup Operators in Monte-Carlo Tree Search

Coulom

2007

Lecture Notes in Computer Science

889

770

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Abstract. Monte-Carlo evaluation consists in estimating a position by averaging the outcome of several random continuations, and can serve as an evaluation function at the leaves of a min-max tree. This paper presents a new framework to combine tree search with Monte-Carlo evaluation, that does not separate between a min-max phase and a MonteCarlo phase. Instead of backing-up the min-max value close to the root, and the average value at some depth, a more general backup operator is defined that progressively changes from averaging to min-max as the number of simulations grows. This approach provides a fine-grained control of the tree growth, at the level of individual simulations, and allows efficient selectivity methods. This algorithm was implemented in a 9 × 9 Go-playing program, Crazy Stone, that won the 10th KGS computer-Go tournament.

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Section: Introductionmentioning

confidence: 99%

“…Bouzy [9,7] used this principle to propose progressive pruning. Progressive pruning cuts off moves whose probability of being best according to the distribution of the central-limit theorem falls below some threshold.…”

Section: Introductionmentioning

confidence: 99%

Efficient Selectivity and Backup Operators in Monte-Carlo Tree Search

Coulom

2007

Lecture Notes in Computer Science

889

770

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“…Then, in 2003, Bouzy and Helmstetter reported on further experiments with Monte Carlo playouts, again stressing the advantage of having a program that can play Go moves without the need for a heuristic evaluation function [2,5]. They tried adding a small 2-level minimax tree on top of the random playouts, but this did not improve the performance.…”

Section: Monte Carlo Tree Searchmentioning

confidence: 99%

Investigations with Monte Carlo Tree Search for Finding Better Multivariate Horner Schemes

Herik

Kuipers

Vermaseren

et al. 2014

Communications in Computer and Information Science

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Abstract. After a computer chess program had defeated the human World Champion in 1997, many researchers turned their attention to the oriental game of Go. It turned out that the minimax approach, so successful in chess, did not work in Go. Instead, after some ten years of intensive research, a new method was developed: MCTS (Monte Carlo Tree Search), with promising results. MCTS works by averaging the results of random play-outs. At first glance it is quite surprising that MCTS works so well. However, deeper analysis revealed the reasons.The success of MCTS in Go caused researchers to apply the method to other domains. In this article we report on experiments with MCTS for finding improved orderings for multivariate Horner schemes, a basic method for evaluating polynomials. We report on initial results, and continue with an investigation into two parameters that guide the MCTS search. Horner's rule turns out to be a fruitful testbed for MCTS, allowing easy experimentation with its parameters. The results reported here provide insight into how and why MCTS works. It will be interesting to see if these insights can be transferred to other domains, for example, back to Go.

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“…However, when full search is too slow, Monte-Carlo sampling is useful for improving heuristic evaluation as well. Such methods are popular in games with incomplete information such as Poker (Billings et al, 2002), and also in games with complete information such as Go (Bouzy and Helmstetter, 2003). Abramson's expected-outcome evaluation (Abramson, 1990) evaluates a node in a search tree by averaging the values of terminal nodes reached from it through random play.…”

Section: Monte-carlo Sampling For Heuristic Interior Node Evaluationmentioning

confidence: 99%

Solving Probabilistic Combinatorial Games

Zhao

Müller

2006

Lecture Notes in Computer Science

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Probabilistic combinatorial games (PCG) are a model for Go-like games recently introduced by Ken Chen. They differ from normal combinatorial games since terminal position in each subgame are evaluated by a probability distribution. The distribution expresses the uncertainty in the local evaluation. This paper focuses on the analysis and solution methods for a special case, 1-level binary PCG. Monte-Carlo analysis is used for move ordering in an exact solver, that can compute the winning probability of a PCG efficiently. Monte-Carlo interior evaluation is used in a heuristic player. Experimental results show that both types of Monte-Carlo methods work very well in this problem.

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Monte-Carlo Go Developments

Cited by 78 publications

References 13 publications

Efficient Selectivity and Backup Operators in Monte-Carlo Tree Search

Efficient Selectivity and Backup Operators in Monte-Carlo Tree Search

Investigations with Monte Carlo Tree Search for Finding Better Multivariate Horner Schemes

Solving Probabilistic Combinatorial Games

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