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Erschienen in:
2008 | OriginalPaper | Buchkapitel
Knowledge Generation for Improving Simulations in UCT for General Game Playing
verfasst von : Shiven Sharma, Ziad Kobti, Scott Goodwin
Erschienen in: AI 2008: Advances in Artificial Intelligence
Verlag: Springer Berlin Heidelberg
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General Game Playing (GGP) aims at developing game playing agents that are able to play a variety of games and, in the absence of pre-programmed game specific knowledge, become proficient players. Most GGP players have used standard tree-search techniques enhanced by automatic heuristic learning. The UCT algorithm, a simulation-based tree search, is a new approach and has been used successfully in GGP. However, it relies heavily on random simulations to assign values to unvisited nodes and selecting nodes for descending down a tree. This can lead to slower convergence times in UCT. In this paper, we discuss the generation and evolution of domain-independent knowledge using both state and move patterns. This is then used to guide the simulations in UCT. In order to test the improvements, we create matches between a player using standard the UCT algorithm and one using UCT enhanced with knowledge.