Monte-Carlo Go With Knowledge-Guided Simulations1

Chen, Keh-Hsun; Zhang, Peigang

doi:10.3233/icg-2008-31202

Cited by 12 publications

(6 citation statements)

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“…We then adjust values for local patterns around the last move and second to last move. Finally we add major values to moves for capturing/escaping blocks with one or two liberties using speedy pseudo ladders [10]. Again, successor moves are ordered by the final move values.…”

Section: Knowledge At Non-root Nodesmentioning

confidence: 99%

“…Only essential domain knowledge items that can be computed in lightning speed are suitable for the play-outs [3,10,17].…”

Section: Domain Knowledge and Move-generators For Play-outsmentioning

confidence: 99%

“…Some essential domain knowledge is needed for MCTS to be effective in Go [10,18]. Suitable randomization on key parameters of the UCT algorithm based MCTS and on the priorities of domain knowledge items can increase diversity while keeping the sanity of the simulation games and thus improve the performance of the program significantly.…”

Section: Introductionmentioning

confidence: 98%

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Dynamic randomization and domain knowledge in Monte-Carlo Tree Search for Go knowledge-based systems

Chen

2012

Knowledge-Based Systems

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Section: Knowledge At Non-root Nodesmentioning

confidence: 99%

“…Only essential domain knowledge items that can be computed in lightning speed are suitable for the play-outs [3,10,17].…”

Section: Domain Knowledge and Move-generators For Play-outsmentioning

confidence: 99%

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Dynamic randomization and domain knowledge in Monte-Carlo Tree Search for Go knowledge-based systems

Chen

2012

Knowledge-Based Systems

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“…This succeeding step is called the play-out. In order to improve the quality of the play-outs, the moves are chosen quasi-randomly based on heuristic knowledge [7,15,22]. For SameGame, several simulation strategies exist.…”

Section: Play-out Stepmentioning

confidence: 99%

Single-player Monte-Carlo tree search for SameGame

Schadd

Winands

Tak

et al. 2012

Knowledge-Based Systems

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a b s t r a c tClassic methods such as A ⁄ and IDA ⁄ are a popular and successful choice for one-player games. However, without an accurate admissible evaluation function, they fail. In this article we investigate whether Monte-Carlo tree search (MCTS) is an interesting alternative for one-player games where A ⁄ and IDA ⁄ methods do not perform well. Therefore, we propose a new MCTS variant, called single-player MonteCarlo tree search (SP-MCTS). The selection and backpropagation strategy in SP-MCTS are different from standard MCTS. Moreover, SP-MCTS makes use of randomized restarts. We tested IDA ⁄ and SP-MCTS on the puzzle SameGame and used the cross-entropy method to tune the SP-MCTS parameters. It turned out that our SP-MCTS program is able to score a substantial number of points on the standardized test set.

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“…The play-outs are an estimate for the values of the nodes in the MCTS tree. The use of an adequate simulation strategy (instead of playing randomly) has been shown to improve the level of play significantly (Bouzy, 2005;Chen and Zhang, 2008).…”

Section: Play-out Stepmentioning

confidence: 99%

Selective search in games of different complexity

Schadd

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Monte-Carlo Go With Knowledge-Guided Simulations1

Cited by 12 publications

References 0 publications

Dynamic randomization and domain knowledge in Monte-Carlo Tree Search for Go knowledge-based systems

Dynamic randomization and domain knowledge in Monte-Carlo Tree Search for Go knowledge-based systems

Single-player Monte-Carlo tree search for SameGame

Selective search in games of different complexity

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