Investigating the Effect of Relevance and Reachability Constraints on SAT Encodings of Planning

Recently, satisfiability (SAT) techniques have been shown to be more efficient at extracting solutions from a planning graph in Graphplan than the standard backward search. Graphplan gains efficiency from forward propagation and backward use of mutual exclusion constraints. The utility of SAT techniques for solution extraction raises two important questions: (a) Are the mutual exclusion constraints equally useful for so-lution extraction with SAT encodings? (b) If so, are there additional types of propagated constraints that can benefit them even more? Our ongoing research investigates these two questions.

Authors

• Biplav Srivastava AI Institute, University of South Carolina
• Arizona State University

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