Paper: Learning to Win by Reading Manuals in a Monte-Carlo Framework

ACL ID P11-1028
Title Learning to Win by Reading Manuals in a Monte-Carlo Framework
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2011
Authors

This paper presents a novel approach for lever- aging automatically extracted textual knowl- edge 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 learn- ing game strategies guided by the selected text. Our method operates in the Monte-Carlo search framework, and learns both text anal- ysis and game strategies based only on envi- ronment feedback. We apply our approach to the complex strategy game Civilization II us- ing the official game manual as the text guide. Our results show that a linguistically-informed game-playing agent significantly outperforms its language-unaware counterp...