Abstract. Domain independent planners can produce better-quality plans through the use of domain-speci c knowledge, typically encoded as search c o n trol rules. The planning-by-rewriting approach has been proposed as an alternative t e c hnique for improving plan quality. W e present a system that automatically learns plan rewriting rules and compare it with a system that automatically learns search c o n trol rules for partial order planners. Our results indicate that learning search control rules is a better choice than learning rewrite rules.
REF compare the performance of search control rules versus plan rewriting rules (both learned from problem solving traces).
Learning Rewrite Rules versus Search Control Rules to Improve Plan Quality
Canadian Conference on AI
Computer Science