Abstract
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 language | English (US) |
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Title of host publication | Proceedings of the National Conference on Artificial Intelligence |
Place of Publication | Menlo Park, CA, United States |
Publisher | AAAI |
Pages | 992-997 |
Number of pages | 6 |
Volume | 2 |
State | Published - 1994 |
Event | Proceedings of the 12th National Conference on Artificial Intelligence. Part 1 (of 2) - Seattle, WA, USA Duration: Jul 31 1994 → Aug 4 1994 |
Other
Other | Proceedings of the 12th National Conference on Artificial Intelligence. Part 1 (of 2) |
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City | Seattle, WA, USA |
Period | 7/31/94 → 8/4/94 |
ASJC Scopus subject areas
- Software