Fast Planning through Greedy Action Graphs

Proceedings of the AAAI Conference on Artificial Intelligence, 16

Volume

Proceedings of the AAAI Conference on Artificial Intelligence, 16

Planning

Domain-independent planning is a notoriously hard search problem. Several systematic search techniques have been proposed in the context of various formalisms. However, despite their theoretical completeness, in practice these algorithms are incomplete because for many problems the search space is too large to be (even partially) explored. In this paper we propose a new search method in the context of Blum and Furst’s planning graph approach, which is based on local search. Local search techniques are incomplete, but in practice they can efficiently solve problems that are unsolvable for current systematic search methods. We introduce three heuristics to guide the local search (Walkplan, Tabuplan and T-Walkplan), and we propose two methods for combining local and systematic search. Our techniques are implemented in a system called GPG, which can be used for both plan-generation and plan-adaptation tasks. Experimental results show that GPG can efficiently solve problems that are very hard for current planners based on planning graphs.