The paper illustrates a novel approach to conformant planning using classical planners. The approach relies on two core ideas developed to deal with incomplete information in the initial situation: the use of a classical planner to solve non-classical planning problems, and the reduction of the size of the initial belief state. Differently from previous uses of classical planners to solve non-classical planning problems, the approach proposed in this paper creates a valid plan from a possible plan---by inserting actions into the possible plan and maintaining only one level of non-deterministic choice (i.e., the initial plan being modified). The algorithm can be instantiated with different classical planners---the paper presents the GC[LAMA] implementation, whose classical planner is LAMA. We investigate properties of the approach, including conditions for completeness. GC[LAMA] is empirically evaluated against state-of-the-art conformant planners, using benchmarks from the literature. The experimental results show that GC[LAMA] is superior to other planners, in both performance and scalability. GC[LAMA] is the only planner that can solve the largest instances from several domains. The paper investigates the reasons behind the good performance and the challenges encountered in GC[LAMA].

The paper introduces a novel technique for improving the performance and scalability of best-first progression-based conformant planners. The technique is inspired by different well-known techniques from classical planning, such as landmark and stratification. Its most salient feature is that it is relatively cheap to implement yet quite effective when applicable. The effectiveness of the proposed technique is demonstrated by the development of new conformant planners by integrating the technique in various state-of-the-art conformant planners and an extensive experimental evaluation of the new planners using benchmarks collected from various sources. The result shows that the technique can be applied in several benchmarks and helps improve both performance and scalability of conformant planners.