Planning Graph as a (Dynamic) CSP: Exploiting EBL, DDB and other CSP Search Techniques in Graphlan

Research output: Contribution to journalArticle

51 Scopus citations

Abstract

This paper reviews the connections between Graphplan's planning-graph and the dynamic constraint satisfaction problem and motivates the need for adapting CSP search techniques to the Graphplan algorithm. It then describes how explanation based learning, dependency directed back-tracking, dynamic variable ordering, forward checking, sticky values and random-restart search strategies can be adapted to Graphplan. Empirical results are provided to demonstrate that these augmentations improve Graphplan's performance significantly (up to 1000x speedups)on several benchmark problems. Special attention is paid to the explanation-based learning and dependency directed backtracking techniques as they are empirically found to be most useful in improving the performance of Graphplan.

Original languageEnglish (US)
Number of pages1
JournalJournal of Artificial Intelligence Research
Volume12
DOIs
StatePublished - Jan 1 2000

ASJC Scopus subject areas

  • Artificial Intelligence

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