An online learning method for improving over-subscription planning

Sungwook Yoon, J. Benton, Subbarao Kambhampati

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

1 Scopus citations

Abstract

Despite the recent resurgence of interest in learning methods for planning, most such efforts are still focused exclusively on classical planning problems. In this work, we investigate the effectiveness of learning approaches for improving over-subscription planning, a problem that has received significant recent interest. Viewing over-subscription planning as a domain-independent optimization problem, we adapt the STAGE (Boyan and Moore 2000) approach to learn and improve the plan search. The key challenge in our study is how to automate the feature generation process. In our case, we developed and experimented with a relational feature set, based on Taxonomic syntax as well as a propositional feature set, based on ground-facts. The feature generation process and training data generation process are all automatic, making it a completely domain-independent optimization process that takes advantage of online learning. In empirical studies, our proposed approach improved upon the baseline planner for over-subscription planning on many of the benchmark problems.

Original languageEnglish (US)
Title of host publicationICAPS 2008 - Proceedings of the 18th International Conference on Automated Planning and Scheduling
Pages404-411
Number of pages8
StatePublished - Dec 1 2008
Event18th International Conference on Automated Planning and Scheduling, ICAPS 2008 - Sydney, NSW, Australia
Duration: Sep 14 2008Sep 18 2008

Publication series

NameICAPS 2008 - Proceedings of the 18th International Conference on Automated Planning and Scheduling

Other

Other18th International Conference on Automated Planning and Scheduling, ICAPS 2008
CountryAustralia
CitySydney, NSW
Period9/14/089/18/08

ASJC Scopus subject areas

  • Strategy and Management
  • Artificial Intelligence
  • Computer Science Applications

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