ProbMaxn: Opponent modeling in n-player games
- Nathan R. Sturtevant
- Martin Zinkevich
- Michael Bowling, University of Alberta
Much of the work on opponent modeling for game tree search has been unsuccessful. In two-player, zero-sum games, the gains from opponent modeling are often outweighed by the cost of modeling. Opponent modeling solutions simply cannot search as deep as the highly optimized minimax search with alpha-beta pruning. Recent work has begun to look at the need for opponent modeling in n-player or generalsum games. We introduce a probabilistic approach to opponent modeling in n-player games called prob-maxn, which can robustly adapt to unknown opponents. We implement prob-maxn in the game of Spades, showing that prob-maxn is highly effective in practice, beating out the maxn and softmaxn algorithms when faced with unknown opponents.
Citation
N. Sturtevant, M. Zinkevich, M. Bowling. "ProbMaxn: Opponent modeling in n-player games". National Conference on Artificial Intelligence (AAAI), Boston, Massachusetts, USA, pp 1057-1063, January 2006.| Keywords: | machine learning |
| Category: | In Conference |
BibTeX
@incollection{Sturtevant+al:AAAI06,
author = {Nathan R. Sturtevant and Martin Zinkevich and Michael Bowling},
title = {ProbMaxn: Opponent modeling in n-player games},
Pages = {1057-1063},
booktitle = {National Conference on Artificial Intelligence (AAAI)},
year = 2006,
}Last Updated: July 03, 2020Submitted by Sabina P
