An Automatically Generated Evaluation Function in General Game Playing

General game-playing (GGP) competitions provide a framework for building multigame-playing agents. In this paper, we describe an attempt at the implementation of such an agent. It relies heavily on our knowledge-free method of automatic construction of an approximate state evaluation function, based...

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Veröffentlicht in:IEEE transactions on computational intelligence and AI in games. Jg. 6; H. 3; S. 258 - 270
Hauptverfasser: Waledzik, Karol, Mandziuk, Jacek
Format: Journal Article
Sprache:Englisch
Veröffentlicht: IEEE 01.09.2014
Schlagworte:
Algorithm design and analysis
Approximation
Approximation algorithms
Artificial intelligence
Autonomous game playing
Buildings
Construction
Correlation
evaluation function
Games
general game playing (GGP)
Intelligence
Law
Mathematical analysis
Mathematical models
MTD
Stability analysis
Trees
upper confidence bounds applied to trees (UCT)
ISSN:1943-068X, 1943-0698
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Zusammenfassung:General game-playing (GGP) competitions provide a framework for building multigame-playing agents. In this paper, we describe an attempt at the implementation of such an agent. It relies heavily on our knowledge-free method of automatic construction of an approximate state evaluation function, based on game rules only. This function is then employed by one of the two game tree search methods: MTD (f) or guided upper confidence bounds applied to trees (GUCT), the latter being our proposal of an algorithm combining UCT with the usage of an evaluation function. The performance of our agent is very satisfactory when compared to a baseline UCT implementation.
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ISSN:1943-068X
1943-0698
DOI:10.1109/TCIAIG.2013.2286825