Robust game play against unknown opponents
- Nathan R. Sturtevant
- Michael Bowling, University of Alberta
A standard assumption of search in two-player games is that the opponent has the same evaluation function or utility for possible game outcomes. While some work has been done to try to better exploit weak opponents, it has only been a minor component of high-performance game playing programs such as Chinook or Deep Blue. However, we demonstrate that in games with more than two players, opponent modeling is a necessary component for ensuring high-quality play against unknown opponents. Thus, we propose a new algorithm, soft-maxn, which can help accommodate differences in opponent styles. Finally, we show an inference mechanism that can be used with soft-maxn to infer the playing style of our opponents.
Citation
N. Sturtevant, M. Bowling. "Robust game play against unknown opponents". Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS), pp 713-719, January 2006.| Keywords: | machine learning |
| Category: | In Conference |
BibTeX
@incollection{Sturtevant+Bowling:AAMAS06,
author = {Nathan R. Sturtevant and Michael Bowling},
title = {Robust game play against unknown opponents},
Pages = {713-719},
booktitle = {Joint Conference on Autonomous Agents and Multi-Agent Systems
(AAMAS)},
year = 2006,
}Last Updated: July 03, 2020Submitted by Sabina P
