Pruning Playouts in Monte-Carlo Tree Search for the Game of Havannah

Duguépéroux, Joris; Mazyad, Ahmad; Teytaud, Fabien; Dehos, Julien

doi:10.1007/978-3-319-50935-8_5

Cited by 6 publications

(4 citation statements)

References 22 publications

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“…Variants of this algorithm have been successfully applied to a variety of games (e.g. Havannah [13], Amazons [14], Lines of Action [15], Hex [16], Go [17], chess and Shogi [18]) and represent the state-of-the-art approach for many of them. The algorithm is capable of learning to play promising moves in a turn-based game.…”

Section: Monte Carlo Tree Search For Game Playingmentioning

confidence: 99%

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Exploring search space trees using an adapted version of Monte Carlo tree search for combinatorial optimization problems

Jooken

Leyman²,

Wauters³

et al. 2023

Computers & Operations Research

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Section: Monte Carlo Tree Search For Game Playingmentioning

confidence: 99%

“…Dataset B: comparison between different versions of MCTS where a subset of enhancements are omitted (versions[13][14][15][16]. Smaller objective function values are better.…”

mentioning

confidence: 99%

Exploring search space trees using an adapted version of Monte Carlo tree search for combinatorial optimization problems

Jooken

Leyman²,

Wauters³

et al. 2023

Computers & Operations Research

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“…MCTS space exponentially increases with the number of iterations and nodes in the tree. Different kinds of pruning techniques: probability-based [17,44], heuristic-based [45] reduce MCTS space in two players game. Neil et al [4] designed a single-player game whose objective is to transform an initial phase into a set of goal conditions phase using automatic move pruning.…”

Section: Personalized Itinerary Recommendation With Queuing Time Awar...mentioning

confidence: 99%

Efficient itinerary recommendation via personalized POI selection and pruning

et al. 2022

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Personalized itinerary recommendation has garnered wide research interests for their ubiquitous applications. Recommending personalized itineraries is complex because of the large number of points of interest (POI) to consider in order to construct an itinerary based on visitors’ interest and preference, time budget and uncertain queuing time. Previous studies typically aim to plan itineraries that maximize POI popularity, visitors’ interest and minimize queuing time. However, existing solutions may not reflect visitor preferences because when creating itineraries, they prefer to recommend POIs with short prior visiting periods. These recommendations can conflict with real-life scenarios as visitors typically spend less time at POIs that they do not enjoy, thus leading to the inclusion of unsuitable POIs. Moreover, constructing itineraries based on selected POIs is a challenging and time-consuming process. Existing approaches involve searching through a large number of non-optimal, duplicate itineraries that are time-consuming to review and generate. To address these issues, we propose an adaptive Monte Carlo tree search (MCTS)-based reinforcement learning algorithm EffiTourRec using an effective POI selection strategy by giving preference to POIs with long visiting times and short queuing times along with high POI popularity and visitor interest. In addition, to reduce non-optimal and duplicated itineraries generation, we propose an efficient MCTS search pruning technique to explore a smaller, more promising portion of solution space. Experiment results in real theme park datasets show clear advantages of our proposed method over baselines, where our method outperforms the current state-of-the-art by 20.89 to 52.32% in precision, 8.36 to 21.35% in F1-score and 40.00 to 67.64% in execution time.

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“…It has been successfully applied to a variety of games (e.g. Havannah [7], Amazons [14], Lines of Action [30], Hex [1], Go [24], chess and Shogi [25]). The algorithm is capable of learning to perform promising moves in a turnbased game.…”

Section: Monte Carlo Tree Search For Game Playingmentioning

confidence: 99%

Exploring search space trees using an adapted version of Monte Carlo tree search for combinatorial optimization problems

Jooken¹,

Leyman²,

Causmaecker³

et al. 2020

Preprint

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In this article, a novel approach to solve combinatorial optimization problems is proposed. This approach makes use of a heuristic algorithm to explore the search space tree of a problem instance. The algorithm is based on Monte Carlo tree search, a popular algorithm in game playing that is used to explore game trees. By leveraging the combinatorial structure of a problem, several enhancements to the algorithm are proposed. These enhancements aim to efficiently explore the search space tree by pruning subtrees, using a heuristic simulation policy, reducing the domain of variables by eliminating dominated solutions and using a beam width. They are demonstrated for a specific combinatorial optimization problem: the quay crane scheduling problem with non-crossing constraints. Computational results show that the proposed algorithm is competitive with the state-of-the-art for this problem and eight new best solutions for a benchmark set of instances are found. Apart from this, the results also show evidence that the algorithm is able to learn to correct the incorrect choices of a standard heuristic, yielding an average improvement of 10.0% with respect to the objective function value of the solution.

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Pruning Playouts in Monte-Carlo Tree Search for the Game of Havannah

Cited by 6 publications

References 22 publications

Exploring search space trees using an adapted version of Monte Carlo tree search for combinatorial optimization problems

Exploring search space trees using an adapted version of Monte Carlo tree search for combinatorial optimization problems

Efficient itinerary recommendation via personalized POI selection and pruning

Exploring search space trees using an adapted version of Monte Carlo tree search for combinatorial optimization problems

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