Strong Cyclic Planning with Incomplete Information and Sensing

Incomplete information and sensing are needed in order to design agents that operate in domains where their information acquisition capabilities are restricted and the environment may evolve in unpredictable ways. These representation issues, that have been studied by the work on reasoning about actions, are also being addressed from a planning perspective. The aim of this paper is to present a language for expressing planning domains with incomplete knowledge and sensing and by providing a new technique for generating cyclic plans in such a framework. The basic feature of the representation is the notion of belief state which is characterized in terms of epistemic formulas, representing the knowledge of the agent about the environment. The proposed planning method deals with such belief states using propositional reasoning as a building block