Paper: Learning High-Level Planning from Text

ACL ID P12-1014
Title Learning High-Level Planning from Text
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2012
Authors

Comprehending action preconditions and ef- fects is an essential step in modeling the dy- namics of the world. In this paper, we ex- press the semantics of precondition relations extracted from text in terms of planning oper- ations. The challenge of modeling this con- nection is to ground language at the level of relations. This type of grounding enables us to create high-level plans based on language ab- stractions. Our model jointly learns to predict precondition relations from text and to per- form high-level planning guided by those rela- tions. We implement this idea in the reinforce- ment learning framework using feedback au- tomatically obtained from plan execution at- tempts. When applied to a complex virtual world and text describing that world, our rela- tion extraction techniqu...