Single-Player Monte-Carlo Tree Search

Schadd, Maarten P. D.; Winands, Mark H. M.; Herik, H.J. van den; Chaslot, Guillaume; Uiterwijk, Jos W. H. M.

doi:10.1007/978-3-540-87608-3_1

Cited by 79 publications

(63 citation statements)

References 8 publications

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“…For many other application domains the construction of a heuristic evaluation function is an obstacle, too. Therefore we expect that there are many other domains that could benefit from the MCTS technology, and, indeed, many other applications have already been found how to adapt MCTS to fit their characteristics (see, for example, [6,13,28,31,32,40,41,43]). In this paper one such adaptation has been discussed, viz.…”

Section: Discussionmentioning

confidence: 99%

“…The ability of MCTS to magically home in on clusters of "bright spots" in the state space without relying on domain knowledge has resulted in a long list of other applications, for example, for proofnumber search [40]. In addition, MCTS has been proposed as a new framework for game-AI for video games [13], for the game Settlers of Catan [43], for the game Einstein würfelt nicht [32], for the Voronoi game [6], for Havannah [31], for Amazons [28], and for various single player applications [39,41].…”

Section: Applications Beyond Gomentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

Section: Applications Beyond Gomentioning

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

Self Cite

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“…MCTS has also been applied to single-player games, like SameGame [12], where the player's goal is to destroy contiguous tiles of the same colour, distributed in a rectangular grid. Another use of MCTS is in the popular puzzle Morpion Solitaire [13], a connection game where the goal is to link nodes of a graph with straight lines that must contain at least five vertices.…”

Section: Monte Carlo Tree Searchmentioning

confidence: 99%

Multiobjective Monte Carlo Tree Search for Real-Time Games

Pérez

Mostaghim

Samothrakis

et al. 2015

IEEE Trans. Comput. Intell. AI Games

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Abstract-Multi-objective optimization has been traditionally a matter of study in domains like engineering or finance, with little impact on games research. However, action-decision based on multi-objective evaluation may be beneficial in order to obtain a high quality level of play. This paper presents a Multi-objective Monte Carlo Tree Search algorithm for planning and control in real-time game domains, those where the time budget to decide the next move to make is close to 40ms. A comparison is made between the proposed algorithm, a single-objective version of Monte Carlo Tree Search and a rolling horizon implementation of Non-dominated Sorting Evolutionary Algorithm II (NSGA-II). Two different benchmarks are employed, Deep Sea Treasure and the Multi-Objective Physical Travelling Salesman Problem. Using the same heuristics on each game, the analysis is focused on how well the algorithms explore the search space. Results show that the algorithm proposed outperforms NSGA-II. Additionally, it is also shown that the algorithm is able to converge to different optimal solutions or the optimal Pareto front (if achieved during search).

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“…The Single-Player MCTS (SP-MCTS) was introduced by Shadd et al [13], where a modification of UCT was proposed in order to include the effect of not having an opponent to play against. The authors found that restarting the seed of the random simulations periodically, while saving the best solution found so far, increases the performance of the algorithm for single player games.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The authors found that restarting the seed of the random simulations periodically, while saving the best solution found so far, increases the performance of the algorithm for single player games. Another puzzle, SameGame, has also been addressed by many researchers, including Schadd et al [13]. They used SP-MCTS with modified back propagation, parameter tuning and a meta-search extension, to obtain the highest score ever obtained by any AI player so far.…”

Section: Literature Reviewmentioning

confidence: 99%

Monte-Carlo Tree Search for the Physical Travelling Salesman Problem

Pérez

Rohlfshagen

Lucas

2012

Lecture Notes in Computer Science

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Abstract. The significant success of MCTS in recent years, particularly in the game Go, has led to the application of MCTS to numerous other domains. In an ongoing effort to better understand the performance of MCTS in open-ended realtime video games, we apply MCTS to the Physical Travelling Salesman Problem (PTSP). We discuss different approaches to tailor MCTS to this particular problem domain and subsequently identify and attempt to overcome some of the apparent shortcomings. Results show that suitable heuristics can boost the performance of MCTS significantly in this domain. However, visualisations of the search indicate that MCTS is currently seeking solutions in a rather greedy manner, and coercing it to balance short term and long term constraints for the PTSP remains an open problem.

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Single-Player Monte-Carlo Tree Search

Cited by 79 publications

References 8 publications

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

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

Multiobjective Monte Carlo Tree Search for Real-Time Games

Monte-Carlo Tree Search for the Physical Travelling Salesman Problem

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