Abstract This paper proposes a generic approach to planning in the presence of incomplete information. The approach builds on an abstract notion of a belief state representation, along with an associated set of basic operations. These operations facilitate the development of a sound and complete transition function, for reasoning about effects of actions in the presence of incomplete information, and a set of abstract algorithms for planning. The paper demonstrates how the abstract defifinitions and algorithms can be instantiated in three concrete representations: minimal-DNF, minimalCNF, and prime implicates, resulting in three highly competitive conformant planners: DNF, CNF, and PIP. The paper includes an experimental evaluation of the planners DNF, CNF, and PIP and proposes a new set of conformant planning benchmarks that are challenging for state-of-the-art conformant planners