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Learning to win by reading manuals in a Monte-Carlo framework

Branavan, SRK; Silver, D; Barzilay, R; (2011) Learning to win by reading manuals in a Monte-Carlo framework. In: (pp. pp. 268-277).

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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 builtin AI of Civilization II. © 2011 Association for Computational Linguistics.

Type: Proceedings paper
Title: Learning to win by reading manuals in a Monte-Carlo framework
ISBN-13: 9781932432879
URI: http://discovery.ucl.ac.uk/id/eprint/1347379
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