Constructing hierarchical task models using invariance analysis

Abstract

Hierarchical Task Networks (HTNs) are a common model for encoding knowledge about planning domains in the form of task decompositions. We present a novel algorithm that uses invariant analysis to construct an HTN from the PDDL description of a planning domain and a single representative instance. The algorithm defines two types of composite tasks that interact to achieve the goal of a planning instance. One type of task achieves fluents by traversing invariants in which only one fluent can be true at a time. The other type of task applies a single action, which first involves ensuring that the precondition of the action holds. The resulting HTN can be applied to any instance of the planning domain, and is provably sound. We show that the performance of our algorithm is comparable to algorithms that learn HTNs from examples and use added knowledge.