Learning explanation-based search control rules for partial order planning

Suresh Katukam, Subbarao Kambhampati

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

11 Scopus citations

Abstract

This paper presents SNLP+EBL, the first implementation of explanation based learning techniques for a partial order planner. We describe the basic learning framework of SNLP+EBL, including regression, explanation propagation and rule generation. We then concentrate on SNLP+EBL's ability to learn from failures and present a novel approach that uses stronger domain and planner specific consistency checks to detect, explain and learn from the failures of plans at depth limits. We will end with an empirical evaluation of the efficacy of this approach in improving planning performance.

Original languageEnglish (US)
Title of host publicationProceedings of the National Conference on Artificial Intelligence
Place of PublicationMenlo Park, CA, United States
PublisherAAAI
Pages582-587
Number of pages6
Volume1
Publication statusPublished - 1994
EventProceedings of the 12th National Conference on Artificial Intelligence. Part 1 (of 2) - Seattle, WA, USA
Duration: Jul 31 1994Aug 4 1994

Other

OtherProceedings of the 12th National Conference on Artificial Intelligence. Part 1 (of 2)
CitySeattle, WA, USA
Period7/31/948/4/94

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ASJC Scopus subject areas

  • Software

Cite this

Katukam, S., & Kambhampati, S. (1994). Learning explanation-based search control rules for partial order planning. In Proceedings of the National Conference on Artificial Intelligence (Vol. 1, pp. 582-587). Menlo Park, CA, United States: AAAI.