Similarity Measures based on Local Game Trees

Evans, Sabrina, Turrini, Paolo

arXiv.org Artificial Intelligence 

We study strategic similarity of game positions in Contribution We study similarity of game positions in two-player extensive games of perfect information, two-player, deterministic games of perfect information, by by looking at the structure of their local game trees, looking at the structure of their local game trees, working with the aim of improving the performance of game with the set of possible moves from each position. We introduce playing agents in detecting forcing continuations.

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