Learning user plan preferences obfuscated by feasibility constraints

Nan Li, William Cushing, Subbarao Kambhampati, Sungwook Yoon

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

1 Scopus citations

Abstract

It has long been recognized that users can have complex preferences on plans. Non-intrusive learning of such preferences by observing the plans executed by the user is an attractive idea. Unfortunately, the executed plans are often not a true representation of user preferences, as they result from the interaction between user preferences and feasibility constraints. In the travel planning scenario, a user whose true preference is to travel by a plane may well be frequently observed traveling by car because of feasibility constraints (perhaps the user is a poor graduate student). In this work, we describe a novel method for learning true user preferences obfuscated by such feasibility constraints. Our base learner induces probabilistic hierarchical task networks (pHTNs) from sets of training plans. Our approach is to rescale the input so that it represents the user's preference distribution on plans rather than the observed distribution on plans.

Original languageEnglish (US)
Title of host publicationICAPS 2009 - Proceedings of the 19th International Conference on Automated Planning and Scheduling
Pages370-373
Number of pages4
StatePublished - Dec 1 2009
Event19th International Conference on Automated Planning and Scheduling, ICAPS 2009 - Thessaloniki, Greece
Duration: Sep 19 2009Sep 23 2009

Publication series

NameICAPS 2009 - Proceedings of the 19th International Conference on Automated Planning and Scheduling

Other

Other19th International Conference on Automated Planning and Scheduling, ICAPS 2009
CountryGreece
CityThessaloniki
Period9/19/099/23/09

ASJC Scopus subject areas

  • Strategy and Management

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