Temperature Discovery Search
- Martin Mueller
- Markus Enzenberger, Department of Computing Science, University of Alberta
- Jonathan Schaeffer, Department of Computing Science, University of Alberta
Temperature Discovery Search (TDS) is a new minimaxbased 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 aB searcher
Citation
M. Mueller, M. Enzenberger, J. Schaeffer. "Temperature Discovery Search". National Conference on Artificial Intelligence (AAAI), San Jose, California, USA, pp 658-663, January 2004.| Keywords: | Temperature Discovery Search, combinatorial games, Go, Amazons, machine learning |
| Category: | In Conference |
BibTeX
@incollection{Mueller+al:AAAI04,
author = {Martin Mueller and Markus Enzenberger and Jonathan Schaeffer},
title = {Temperature Discovery Search},
Pages = {658-663},
booktitle = {National Conference on Artificial Intelligence (AAAI)},
year = 2004,
}Last Updated: April 24, 2007Submitted by Christian Smith
