Current Frontiers in Computer Go

Rimmel, Arpad; Teytaud, Olivier; Lee, Chang-Shing; Yen, Shi-Jim; Wang, Mei-Hui; Tsai, Shang-Rong

doi:10.1109/tciaig.2010.2098876

Cited by 56 publications

(26 citation statements)

References 28 publications

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“…The vast amount of positions paired with the absence of strong evaluation functions makes Go extremely challenging and made it a popular testbed for AI research in recent years. A great deal of information on current developments in Computer Go and the most often used MCTS algorithms can be found in [14] resp. [15].…”

Section: A Game Of Gomentioning

confidence: 99%

Comparison of Bayesian move prediction systems for Computer Go

Wistuba

Schaefers

Platzner

2012

2012 IEEE Conference on Computational Intelligence and Games (CIG)

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Abstract-Since the early days of research on Computer Go, move prediction systems are an important building block for Go playing programs. Only recently, with the rise of Monte Carlo Tree Search (MCTS) algorithms, the strength of Computer Go programs increased immensely while move prediction remains to be an integral part of state of the art programs. In this paper we review three Bayesian move prediction systems that have been published in recent years and empirically compare them under equal conditions. Our experiments reveal that, given identical input data, the three systems can achieve almost identical prediction rates while differing substantially in their needs for computational and memory resources. From the analysis of our results, we are able to further improve the prediction rates for all three systems.

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Section: A Game Of Gomentioning

confidence: 99%

Comparison of Bayesian move prediction systems for Computer Go

Wistuba

Schaefers

Platzner

2012

2012 IEEE Conference on Computational Intelligence and Games (CIG)

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“…2,5 In 2009, Silver et al 34 developed two algorithms for balancing the simulation policy according to a descending gradient in order to maintain an accurate spread of simulation outcomes. Programs such as MoGo 37 /MoGoTW, Crazy advancements in AI. First, human learning is distinguished by the range and complexity of skills that can be learned and the degree of abstraction that can be achieved relative to other species.…”

Section: Introductionmentioning

confidence: 99%

FML-based Dynamic Assessment Agent for Human-Machine Cooperative System on Game of Go

Lee

Wang

Yang

et al. 2017

Int. J. Unc. Fuzz. Knowl. Based Syst.

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In this paper, we demonstrate the application of Fuzzy Markup Language (FML) to construct an FML-based Dynamic Assessment Agent (FDAA), and we present an FML-based Human-Machine Cooperative System (FHMCS) for the game of Go. The proposed FDAA comprises an intelligent decision-making and learning mechanism, an intelligent game bot, a proximal development agent, and an intelligent agent. The intelligent game bot is based on the open-source code of Facebook's Darkforest, and it features a representational state transfer application programming interface mechanism. The proximal development agent contains a dynamic assessment mechanism, a GoSocket mechanism, and an FML engine with a fuzzy knowledge base and rule base. The intelligent agent contains a GoSocket engine and a summarization agent that is based on the estimated win rate, real-time simulation number, and matching degree of predicted moves.

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“…From the inception of the field to the present, a broad corpus of literature has been published on this topic, introducing a wide range of strategies to achieve effective game play, in a wide range of board games [1][2][3]. In particular, one of the most studied and acclaimed techniques is the alpha-beta search, which is capable of achieving a much greater look-ahead, or search depth, in game trees, by pruning large sections of the search space [4,5].…”

Section: Introductionmentioning

confidence: 99%

Challenging Established Move Ordering Strategies with Adaptive Data Structures

Polk

Oommen

2016

Trends in Applied Knowledge-Based Systems and Data Science

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Abstract. The field of game playing is a particularly well-studied area within the context of AI, leading to the development of powerful techniques, such as the alpha-beta search, capable of achieving competitive game play against an intelligent opponent. It is well known that tree pruning strategies, such as alpha-beta, benefit strongly from proper move ordering, that is, searching the best element first. Inspired by the formerly unrelated field of Adaptive Data Structures (ADSs), we have previously introduced the History-ADS technique, which employs an adaptive list to achieve effective and dynamic move ordering, in a domain independent fashion, and found that it performs well in a wide range of cases. However, previous work did not compare the performance of the History-ADS heuristic to any established move ordering strategy. In an attempt to address this problem, we present here a comparison to two well-known, acclaimed strategies, which operate on a similar philosophy to the History-ADS, the History Heuristic, and the Killer Moves technique. We find that, in a wide range of two-player and multi-player games, at various points in the game's progression, the History-ADS performs at least as well as these strategies, and, in fact, outperforms them in the majority of cases.

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Current Frontiers in Computer Go

Cited by 56 publications

References 28 publications

Comparison of Bayesian move prediction systems for Computer Go

Comparison of Bayesian move prediction systems for Computer Go

FML-based Dynamic Assessment Agent for Human-Machine Cooperative System on Game of Go

Challenging Established Move Ordering Strategies with Adaptive Data Structures

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