Monte-Carlo Tree Search in Settlers of Catan

Szita, István; Chaslot, Guillaume; Spronck, Pieter

doi:10.1007/978-3-642-12993-3_3

Cited by 64 publications

(73 citation statements)

References 11 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

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show abstract

“…Although multi-player games can be thought of as a generalization of the two-player environment to an Nplayer case, the majority of research has continued to focus on two-player games, with a substantially lesser focus on MPGP being present in the literature [9,22,27,28,31]. However, through the addition of multiple self-interested agents, such games present a number of complications and challenges that are not present in traditional two-player game playing.…”

Section: Previous Work: the Threat-ads Heuristicmentioning

confidence: 99%

On Achieving History-Based Move Ordering in Adversarial Board Games Using Adaptive Data Structures

Polk

Oommen

2016

Lecture Notes in Computer Science

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This paper concerns the problem of enhancing the well-known alpha-beta search technique for intelligent game playing. It is a well-established principle that the alpha-beta technique benefits greatly, that is to say, achieves more efficient tree pruning, if the moves to be examined are ordered properly. This refers to placing the best moves in such a way that they are searched first. However, if the superior moves were known a priori, there would be no need to search at all. Many move ordering heuristics, such as the Killer Moves technique and the History Heuristic, have been developed in an attempt to address this problem.Formerly unrelated to game playing, the field of Adaptive Data Structures (ADSs) is concerned with the optimization of queries over time within a data structure, and provides techniques to achieve this through dynamic reordering of its internal elements, in response to queries. In earlier works, we had proposed the Threat-ADS heuristic for multi-player games, based on the concept of employing efficient ranking mechanisms provided by ADSs in the context of game playing. Based on its previous success, in this work we propose the concept of using an ADS to order moves themselves, rather than opponents. We call this new technique the History-ADS heuristic. We examine the History-ADS heuristic in both two-player and multi-player environments, and investigate its possible refinements. These involve providing a bound on the size of the ADS, based on the hypothesis that it can retain most of its benefits with a smaller list, and examining the possibility of using a different ADS for each level of the tree. We demonstrate conclusively that the History-ADS heuristic can produce drastic improvements in tree pruning in both two-player and multi-player games, and the majority of its benefits remain even when it is limited to a very small list.

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“…Previous works have been done on complex games, but authors often work on a limited version of the game, generally by using only a subset of the rules. For instance, in the Settlers of Catan board game [13] no trade interaction between players was specified, or in Dominion [15] only a subset of the possible cards was used. However, more recently a competitive MCTS player has been designed for the game of 7wonders [11].…”

Section: Introductionmentioning

confidence: 99%