Learning probabilistic hierarchical task networks to capture user preferences

Nan Li, Subbarao Kambhampati, Sungwook Yoon

Research output: Chapter in Book/Report/Conference proceedingConference contribution

21 Scopus citations

Abstract

While much work on learning in planning focused on learning domain physics (i.e., action models), and search control knowledge, little attention has been paid towards learning user preferences on desirable plans. Hierarchical task networks (HTN) are known to provide an effective way to encode user prescriptions about what constitute good plans. However, manual construction of these methods is complex and error prone. In this paper, we propose a novel approach to learning probabilistic hierarchical task networks that capture user preferences by examining user-produced plans given no prior information about the methods (in contrast, most prior work on learning within the HTN framework focused on learning "method preconditions" - i.e., domain physics - assuming that the structure of the methods is given as input). We will show that this problem has close parallels to the problem of probabilistic grammar induction, and describe how grammar inductionmethods can be adapted to learn task networks. We will empirically demonstrate the effectiveness of our approach by showing that task networks we learn are able to generate plans with a distribution close to the distribution of the userpreferred plans.

Original languageEnglish (US)
Title of host publicationIJCAI-09 - Proceedings of the 21st International Joint Conference on Artificial Intelligence
PublisherInternational Joint Conferences on Artificial Intelligence
Pages1754-1759
Number of pages6
ISBN (Print)9781577354260
StatePublished - Jan 1 2009
Event21st International Joint Conference on Artificial Intelligence, IJCAI 2009 - Pasadena, United States
Duration: Jul 11 2009Jul 16 2009

Publication series

NameIJCAI International Joint Conference on Artificial Intelligence
ISSN (Print)1045-0823

Conference

Conference21st International Joint Conference on Artificial Intelligence, IJCAI 2009
Country/TerritoryUnited States
CityPasadena
Period7/11/097/16/09

ASJC Scopus subject areas

  • Artificial Intelligence

Fingerprint

Dive into the research topics of 'Learning probabilistic hierarchical task networks to capture user preferences'. Together they form a unique fingerprint.

Cite this