The TOAD System for Totally Ordered HTN Planning

We present an approach for translating Totally Ordered Hierarchical Task Network (HTN) planning problems to classical planning problems. While this enables the use of sophisticated classical planning systems to find solutions, we need to overcome the differences in expressiveness of these two planning formalisms. Prior work on this topic did this by translating bounded HTN problems. In contrast, we approximate them, i.e., we change the problem such that every action sequence that is a solution to the HTN problem is also a solution for the classical problem, but the latter might have more solutions. To obtain a sound overall approach, we verify solutions returned by the classical planning system to ensure that they are also solutions to the HTN problem. For translation and approximation, we use techniques introduced to approximate Context-Free Languages by using Finite Automata. We named our system TOAD (Totally Ordered HTN Approximation using DFA). For a subset of HTN problems the translation is even possible without approximation. Whether or not it is necessary is decided based on the property of self-embedding, which comes also from the field of formal languages. We investigate the theoretical connection of self-embedding and tail-recursiveness, a property from the HTN literature used to identify a subclass of HTN planning problems that can be translated to classical planning, and show that it is more general. To guide the classical planner, we introduce a novel heuristic tailored towards our models. We evaluate TOAD on the benchmark set of the 2020 International Planning Competition. Our evaluation shows that (1) most problems can be translated without approximation and that (2) TOAD is competitive with the state of the art in HTN planning.