Mixing search strategies for multi-player games

Authors:
Inon Zuckerman;Ariel Felner;Sarit Kraus
Affiliations:
Computer Science Department, Bar-Ilan University, Ramat-Gan, Israel;Information Systems Engineering, Deutsche Telekom Labs, Ben-Gurion University, Be'er-Sheva, Israel;Computer Science Department, Bar-Ilan University, Ramat-Gan, Israel
Venue:
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Year:
2009

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Abstract

There are two basic approaches to generalize the propagation mechanism of the two-player Minimax search algorithm to multi-player (3 or more) games: the MaxN algorithm and the Paranoid algorithm. The main shortcoming of these approaches is that their strategy is fixed. In this paper we suggest a new approach (called MP-Mix) that dynamically changes the propagation strategy based on the players' relative strengths between MaxN, Paranoid and a newly presented offensive strategy. In addition, we introduce the Opponent Impact factor for multi-player games, which measures the players' ability to impact their opponents' score, and show its relation to the relative performance of our new MP-Mix strategy. Experimental results show that MP-Mix outperforms all other approaches under most circumstances.