Monte Carlo Tree Search with Metaheuristics
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN: 1611-3349, Vol: 14126 LNAI, Page: 134-144
2023
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Conference Paper Description
Monte Carlo Tree Search/Upper Confidence bounds applied to Trees (MCTS/UCT) is a popular and powerful search technique applicable to many domains, most frequently to searching game trees. Even though the algorithm has been widely researched, there is still room for its improvement, especially when combined with metaheuristics or machine learning methods. In this paper, we revise and experimentally evaluate the idea of enhancing MCTS/UCT with game-specific heuristics that guide the playout (simulation) phase. MCTS/UCT with the proposed guiding mechanism is tested on two popular board games: Othello and Hex. The enhanced method clearly defeats the well-known Alpha-beta pruning algorithm in both games, and for the more complex game (Othello) is highly competitive to the vanilla MCTS/UCT formulation.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85174436704&origin=inward; http://dx.doi.org/10.1007/978-3-031-42508-0_13; https://link.springer.com/10.1007/978-3-031-42508-0_13; https://dx.doi.org/10.1007/978-3-031-42508-0_13; https://link.springer.com/chapter/10.1007/978-3-031-42508-0_13
Springer Science and Business Media LLC
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