Derivation replay for partial-order planning

Laurie H. Ihrig, Subbarao Kambhampati

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

13 Scopus citations


Derivation replay was first proposed by Carbonell as a method of transferring guidance from a previous problem-solving episode to a new one. Subsequent implementations have used state-space planning as the underlying methodology. This paper is motivated by the acknowledged superiority of partial-order (PO) planners in plan generation, and is an attempt to bring derivation replay into the realm of partial-order planning. Here we develop DerSNLP, a framework for doing replay in SNLP, a partial-order plan-space planner, and analyze its relative effectiveness. We will argue that the decoupling of planning (derivation) order and the execution order of plan steps, provided by partial-order planners, enables DerSNLP to exploit the guidance of previous cases in a more efficient and straightforward fashion. We validate our hypothesis through empirical comparisons between DerSNLP and two replay systems based on state-space planners.

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


OtherProceedings of the 12th National Conference on Artificial Intelligence. Part 1 (of 2)
CitySeattle, WA, USA

ASJC Scopus subject areas

  • Software


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