Planning with partial preference models

Tuan A. Nguyen, Minh B. Do, Subbarao Kambhampati, Biplav Srivastava

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

9 Scopus citations

Abstract

In many real-world planning scenarios, the users are interested in optimizing multiple objectives (such as makespan and execution cost), but are unable to express their exact tradeoff between those objectives. When a planner encounters such partial preference models, rather than look for a single optimal plan, it needs to present the pareto set of plans and let the user choose from them. This idea of presenting the full pareto set is fraught with both computational and user-interface challenges. To make it practical, we propose the approach of finding a representative subset of the pareto set. We measure the quality of this representative set using the Integrated Convex Preference (ICP) model, originally developed in the OR community. We implement several heuristic approaches based on the Metric-LPG planner to find a good solution set according to this measure. We present empirical results demonstrating the promise of our approach.

Original languageEnglish (US)
Title of host publicationIJCAI-09 - Proceedings of the 21st International Joint Conference on Artificial Intelligence
PublisherInternational Joint Conferences on Artificial Intelligence
Pages1772-1777
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
CountryUnited States
CityPasadena
Period7/11/097/16/09

ASJC Scopus subject areas

  • Artificial Intelligence

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  • Cite this

    Nguyen, T. A., Do, M. B., Kambhampati, S., & Srivastava, B. (2009). Planning with partial preference models. In IJCAI-09 - Proceedings of the 21st International Joint Conference on Artificial Intelligence (pp. 1772-1777). (IJCAI International Joint Conference on Artificial Intelligence). International Joint Conferences on Artificial Intelligence.