Exploiting State Constraints in Heuristic State-Space Planning

In the last years, some very promising domain independent heuristic state-space planners for STRIPS worlds, like ASP/HSP, HSPr and GRT, have been presented. These planners achieve remarkable performance in some domains, like the blocks world, the logistics and the gripper, but they are not effective in other domains, like the grid and the mystery. In this paper we propose the use of state constraints in heuristic state space planning. We claim that one of the causes for the pre-mentioned failures is the absence of domain specific knowledge about properties that characterize every valid and complete state. We propose the inclusion of state constraints in the domain definition and we present how they can be exploited by heuristic planners in order to decompose a problem into subproblems that are easily solvable. We give performance results that exhibit significant speedup in the problem solving process. Finally, we give a notion of how problem decomposition can accelerate other planners, like GRAPHPLAN and BLACKBOX.