A Sampling Approach to Planning with Infinite Domain Control Variables

Abstract

Research in planning has sought to broaden the scope of planning problems by incorporating numeric parameters into action descriptions to condition both continuous and discrete change. Focusing on the latter, this work studies the problem of numeric planning with control variables, a reformulation of actions with infinite domain parameters. To tackle the challenge of handling an infinite decision space driven by control variables, we incorporate sampling into a forward state-space search. The resulting search framework (1) partially expands nodes by sampling their successors and (2) implements a re-expansion strategy to sample additional successors if a node shows promise in future evaluations. We perform a deep probe into this concept that materializes into a new algorithm called Sampling Greedy Best-First Search (S-GBFS). Our empirical evaluation of S-GBFS across various domains shows significant improvements over existing planning approaches.