Assumption-Based Planning with Sensing via Contingent Planning

Assumption-based planning (ABP) is a recently proposed alternative to conformant planning. Like conformant planning, it is designed for domains in which the initial state is uncertain, and in which actions are deterministic. Unlike conformant planning, an ABP plan may make commonsensical assumptions about the initial state, which may result in simpler, easier-to-communicate plans that achieve the goal provided such assumptions hold. In this paper we extend the ABP paradigm to domains with sensing. We propose a polynomial compilation of ABP into an extension of conditional planning supporting negation as failure in action preconditions. We show that extending DNF, a well known conditional planner, with negation as failure, is fairly easy. In our theoretical analysis, we prove that our compilation is polynomial, sound, and complete. In our experimental evaluation we use DNF’s extension and compare with existing, non-polynomial compilations of ABP to conditional planning, showing that in many domains conditional planners are able to solve instances that could be not solved before.