This paper introduces a counter-example based approach for solving probabilistic conformant planning (PCP) problems. Our algorithm incrementally generates candidate plans and identifies counter-examples until it finds a plan for which the probability of success is above the specified threshold. We prove that the algorithm is sound and complete. We further propose a variation of our algorithm that uses hitting sets to accelerate the generation of candidate plans. Experimental results show that our planner is particularly suited for problems with a high probability threshold.

This paper introduces three improvements to the conformant planner CPCES, which continuously searches candidate plans and counter-examples against the current candidate plan until a valid plan (no counter-example exists) is found. First, we identify and merge equivalent PDDL facts to accelerate candidate plan generation. Second, we warm-start CPCES by generating multiple carefully selected counter-examples at the beginning of the procedure, which reduces the number of calls to the classical planner. Third, we investigate the use Fast Downward (FD) as the candidate plan generator; in particular, we propose an incremental procedure to generate the SAS+ file used by FD. Our experimental results show significant improvements for each technique.

In a counter-example based approach to conformant planning, choosing the right counter-example can improve performance. We formalise this observation by introducing the notion of “superiority” of a counter-example over another one, that holds whenever the superior counter-example exhibits more tags than the latter. We provide a theoretical explanation that supports the strategy of searching for maximally superior counter-examples, and we show how this strategy can be implemented. The empirical experiments validate our approach.