Journal ArticleDOI
A Problem Case for UCT
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This paper examines a simple 5 × 5 Hex position that not only completely defeats flat Monte Carlo search, but also initially defeats plain upper confidence bounds for trees (UCT) search until an excessive number of iterations are performed.Abstract:
This paper examines a simple 5 × 5 Hex position that not only completely defeats flat Monte Carlo search, but also initially defeats plain upper confidence bounds for trees (UCT) search until an excessive number of iterations are performed. The inclusion of domain knowledge during playouts significantly improves UCT performance, but a slight negative effect is shown for the rapid action value estimate (RAVE) heuristic under some circumstances. This example was drawn from an actual game during standard play, and highlights the dangers of relying on flat Monte Carlo and unenhanced UCT search even for rough estimates. A brief comparison is made with RAVE failure in Go.read more
Citations
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Proceedings Article
Strategic Features for General Games
TL;DR: An ongoing research project that requires the automated self-play learning and evaluation of a large number of board games in digital form is described, taking to determine relevant features, for biasing MCTS playouts for arbitrary games played on arbitrary geometries.
Journal ArticleDOI
Monte Carlo Tree Search: a review of recent modifications and applications
TL;DR: Monte Carlo Tree Search (MCTS) as discussed by the authors is a powerful approach to designing game-playing bots or solving sequential decision problems, which relies on intelligent tree search that balances exploration and exploitation.
Book ChapterDOI
Positional Games and QBF: The Corrective Encoding
TL;DR: A novel encoding of positional games into Quantified Boolean Formulas (QBFs) such that a game instance admits a winning strategy for first player if and only if the corresponding formula is true.
Posted Content
Monte Carlo Tree Search: A Review of Recent Modifications and Applications.
TL;DR: Monte Carlo Tree Search (MCTS) as mentioned in this paper is a powerful approach to designing game-playing bots or solving sequential decision problems, which relies on intelligent tree search that balances exploration and exploitation.
Journal ArticleDOI
Turn-Based War Chess Model and Its Search Algorithm per Turn
TL;DR: A theory frame involving combinational optimization on the one hand and game tree search on the other hand is proposed and it is proved that both of these algorithms are optimal, and the difference between their efficiencies is analyzed.
References
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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.
Journal ArticleDOI
A Survey of Monte Carlo Tree Search Methods
Cameron Browne,Edward J. Powley,Daniel Whitehouse,Simon M. Lucas,Peter I. Cowling,Philipp Rohlfshagen,S. Tavener,Diego Perez,Spyridon Samothrakis,Simon Colton +9 more
TL;DR: A survey of the literature to date of Monte Carlo tree search, intended to provide a snapshot of the state of the art after the first five years of MCTS research, outlines the core algorithm's derivation, impart some structure on the many variations and enhancements that have been proposed, and summarizes the results from the key game and nongame domains.
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.
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.
Posted Content
Bandit Algorithms for Tree Search
TL;DR: In this article, a bandit algorithm for smooth trees (BAST) is proposed, which takes into account ac- tual smoothness of the rewards for perform- ing efficient "cuts" of sub-optimal branches with high confidence.