AIC Seminar Series

Quantitative temporal constraints are an essential requirement for many AI planning domains. For real-life applications, the HTN planning paradigm has often proven to be better suited than other planning approaches. To date, however, efficiently integrating temporal reasoning with HTN planning has been little explored. We describe a means to exploit the structure of a HTN plan when performing temporal propagation on an associated Simple Temporal Network. By exploiting the natural restriction on permitted temporal constraints, the time complexity of propagation can be sharply reduced, while completeness of the inference is maintained. The approach has been implemented in SRI's PASSAT planning system, where empirical results indicate an order of magnitude improvement on real-world plans.

Neil Yorke-Smith is a Computer Scientist at SRI's Artificial Intelligence Center. He received his Ph.D. from Imperial College London with thesis research focused on handling uncertainty in constraint-based reasoning. His research interests include temporal reasoning, planning and scheduling, advisable agents, and constraint programming, and their real-world applications.

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