Temperature discovery search

Authors:
Martin Müller;Markus Enzenberger;Jonathan Schaeffer
Affiliations:
Department of Computing Science, University of Alberta, Edmonton, Canada;Department of Computing Science, University of Alberta, Edmonton, Canada;Department of Computing Science, University of Alberta, Edmonton, Canada
Venue:
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Year:
2004

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Abstract

Temperature Discovery Search (TDS) is a new minimax-based game tree search method designed to compute or approximate the temperature of a combinatorial game. TDS is based on the concept of an enriched environment, where a combinatorial game G is embedded in an environment consisting of a large set of simple games of decreasing temperature. Optimal play starts in the environment, but eventually must switch to G. TDS finds the temperature of G by determining when this switch must happen. Both exact and heuristic versions of TDS are described and evaluated experimentally. In experiments with sum games in Amazons, TDS outperforms an αβ searcher.