Plan Recovery in Reactive HTNs Using Symbolic Planning

8 years 14 days ago

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Building formal models of the world and using them to plan future action is a central problem in artiﬁcial intelligence. In this work, we combine two well-known approaches to this problem, namely, reactive hierarchical task networks (HTNs) and symbolic linear planning. The practical motivation for this hybrid approach was to recover from breakdowns in HTN execution by dynamically invoking symbolic planning. This work also reﬂects, however, on the deeper issue of tradeoﬀs between procedural and symbolic modeling. We have implemented our approach in a system that combines a reactive HTN engine, called Disco, with a STRIPS planner implemented in Prolog, and conducted a preliminary evaluation.

Lydia Ould Ouali, Charles Rich, Nicolas Sabouret

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