Action Pruning Through Under-approximation Refinement

Planning as heuristic search is the prevalent technique to solve planning problems of any kind of domains. Heuristics estimate distances to goal states in order to guide a search through large state spaces. However, this guidance is often moderate, since still a lot of states lie on plateaus of equally prioritized states in the search topology. Additional techniques that ignore or prefer some actions for solving a problem are successful to support a search in such situations. Nevertheless, some action pruning techniques lead to incomplete searches. We propose an under-approximation refinement framework for adding actions to under-approximations of planning tasks during a search in order to find a plan. For this framework, we develop a refinement strategy. Starting a search on an initial under-approximation of a planning task, the strategy adds actions determined at states close to a goal, whenever the search does not progress towards a goal, until a plan is found. Key elements of this strategy consider helpful actions and relaxed plans for refinements. We have implemented the under-approximation refinement framework into the greedy best first search algorithm. Our results show considerable speedups for many classical planning problems. Moreover, we are able to plan with far less actions than standard greedy best first search.