Heuristic Evaluation Functions for General Game Playing

Clune, James

doi:10.1007/s13218-010-0074-7

Cited by 68 publications

(84 citation statements)

References 6 publications

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“…On the surface, it might appear that our work resembles that of the general game playing community in that we both share the goal of 'learning to play' games [4], [5]. Deeper inspection, however, reveals that the apparent similarity is misleading.…”

Section: Comparison With Related Workmentioning

confidence: 98%

Learning physically-instantiated game play through visual observation

Barbu

Narayanaswamy

Siskind

2010

2010 IEEE International Conference on Robotics and Automation

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Abstract-We present an integrated vision and robotic system that plays, and learns to play, simple physically-instantiated board games that are variants of TIC TAC TOE and HEXA-PAWN. We employ novel custom vision and robotic hardware designed specifically for this learning task. The game rules can be parametrically specified. Two independent computational agents alternate playing the two opponents with the shared vision and robotic hardware, using pre-specified rule sets. A third independent computational agent, sharing the same hardware, learns the game rules solely by observing the physical play, without access to the pre-specified rule set, using inductive logic programming with minimal background knowledge possessed by human children. The vision component of our integrated system reliably detects the position of the board in the image and reconstructs the game state after every move, from a single image. The robotic component reliably moves pieces both between board positions and to and from off-board positions as needed by an arbitrary parametrically-specified legal-move generator. Thus the rules of games learned solely by observing physical play can drive further physical play. We demonstrate our system learning to play six different games.

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Section: Comparison With Related Workmentioning

confidence: 98%

Learning physically-instantiated game play through visual observation

Barbu

Narayanaswamy

Siskind

2010

2010 IEEE International Conference on Robotics and Automation

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“…Despite the above difficulties, several researchers have explored this possibility and some heuristic approaches to General Game Playing used predefined candidates for the evaluation procedure. ClunePlayer [12] considered mobility, payoff and termination stability. Fluxplayer [13], harnessed predefined syntactic templates for common game features like board definition or successor relation.…”

Section: Automatic Construction Of the Evaluation Functionmentioning

confidence: 99%

Generic Heuristic Approach to General Game Playing

Mańdziuk

2012

Lecture Notes in Computer Science

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Abstract. General Game Playing (GGP) is a specially designed environment for creating and testing competitive agents which can play variety of games. The fundamental motivation is to advance the development of various artificial intelligence methods operating together in a previously unknown environment. This approach extrapolates better on real world problems and follows artificial intelligence paradigms better than dedicated single-game optimized solutions. This paper presents a universal method of constructing the heuristic evaluation function for any game playable within the GGP framework. The algorithm embraces distinctive discovery of candidate features to be included in the evaluation function and learning their correlations with actions performed by the players and the game score. Our method integrates well with the UCT algorithm which is currently the state-of-the-art approach in GGP.

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“…Established methods include Monte Carlo tree search [2], the automatic generation of evaluation functions [4] and knowledge acquisition [7]. Several complete general game-playing systems also have been described [12,2].…”

Section: Introductionmentioning

confidence: 99%

Towards General Game-Playing Robots: Models, Architecture and Game Controller

Rajaratnam

Thielscher

2013

Lecture Notes in Computer Science

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Abstract. General Game Playing aims at AI systems that can understand the rules of new games and learn to play them effectively without human intervention. Our paper takes the first step towards general game-playing robots, which extend this capability to AI systems that play games in the real world. We develop a formal model for general games in physical environments and provide a systems architecture that allows the embedding of existing general game players as the "brain" and suitable robotic systems as the "body" of a general game-playing robot. We also report on an initial robot prototype that can understand the rules of arbitrary games and learns to play them in a fixed physical game environment.

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Heuristic Evaluation Functions for General Game Playing

Cited by 68 publications

References 6 publications

Learning physically-instantiated game play through visual observation

Learning physically-instantiated game play through visual observation

Generic Heuristic Approach to General Game Playing

Towards General Game-Playing Robots: Models, Architecture and Game Controller

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