Symbolic Search for Optimal Total-Order HTN Planning

Symbolic search has proven to be a useful approach to optimal classical planning. In Hierarchical Task Network (HTN) planning, however, there is little work on optimal planning. One reason for this is that in HTN planning, most algorithms are based on heuristic search, and admissible heuristics have to incorporate the structure of the task network in order to be informative. In this paper, we present a novel approach to optimal (totally-ordered) HTN planning, which is based on symbolic search. An empirical analysis shows that our symbolic approach outperforms the current state of the art for optimal totally-ordered HTN planning. In this paper we propose a new way of solving HTN planning problems optimally -via symbolic search. In applying symbolic search, we draw from the success of symbolic search in classical planning (Cimatti et al. 1997; Edelkamp, Kissmann, and Torralba 2015) . While explicit search techniques consider individual states as their search nodes, sym-