Learning to win by reading manuals in a Monte-Carlo framework
Name
Barzilay-Learning toWin by Reading.pdf
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359.1 KB
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Checksum (MD5)
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Author(s)
Branavan, Satchuthanan R.
Silver, David
Barzilay, Regina
Date Issued
June 2011
Journal
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1, ACL HLT '11
Publisher
Association for Computing Machinery
Citation
Branavan, S.R.K., David Silver, and Regina Barzilay. "Learning to win by reading manuals in a Monte-Carlo framework." Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1, ACL HLT '11, Portland, Oregon, June 19-24, 2011.
Version
Author's final manuscript
Abstract
This paper presents a novel approach for leveraging automatically extracted textual knowledge to improve the performance of control applications such as games. Our ultimate goal is to enrich a stochastic player with high-level guidance expressed in text. Our model jointly learns to identify text that is relevant to a given game state in addition to learning game strategies guided by the selected text. Our method operates in the Monte-Carlo search framework, and learns both text analysis and game strategies based only on environment feedback. We apply our approach to the complex strategy game Civilization II using the official game manual as the text guide. Our results show that a linguistically-informed game-playing agent significantly outperforms its language-unaware counterpart, yielding a 27% absolute improvement and winning over 78% of games when playing against the built-in AI of Civilization II.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike 3.0
Persistent DSpace Link
DOI of Published Version
http://dl.acm.org/citation.cfm?id=2002507
