Mixing search strategies for multi-player games

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.

[1]  Fredrik Olsson,et al.  Using Multi-Agent System Technologies in Risk Bots , 2006, Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment.

[2]  Richard E. Korf,et al.  On Pruning Techniques for Multi-Player Games , 2000, AAAI/IAAI.

[3]  Keki B. Irani,et al.  An Algorithmic Solution of N-Person Games , 1986, AAAI.

[4]  Richard E. Korf Multi-Player Alpha-Beta Pruning , 1991, Artif. Intell..

[5]  Matthew L. Ginsberg,et al.  GIB: Imperfect Information in a Computationally Challenging Game , 2011, J. Artif. Intell. Res..

[6]  Nathan R. Sturtevant,et al.  A Comparison of Algorithms for Multi-player Games , 2002, Computers and Games.

[7]  Ulf Lorenz,et al.  Player Modeling, Search Algorithms and Strategies in Multi-player Games , 2006, ACG.