Classical planners (e.g., ) presuppose correct and complete information about the world. Although recent work has sketched a number of algorithms for planning with incomplete information (e.g., ), substantial problems remain before these planners can be applied to real-world domains. Since the presence of incomplete information invalidates the Closed World Assumption, an agent cannot deduce that a fact is false based on its absence from the agent's world model. This leads to two challenges:
This paper reports on the fully-implemented XII planner which addresses these challenges. We allow incomplete information, but assume the information that is known is correct. XII's planning algorithm is based on UCPOP , but XII interleaves planning and execution (following IPEM ) and, unlike UCPOP, does not make the closed world assumption.
Section 2 introduces the central concept underlying XII's operation: local closed world information (LCW). In section 3 we describe how incorporating LCW in a planner enables it to solve universally quantified goals in the presence of incomplete information. We then (section 4) show how the same mechanism addresses the problem of redundant information gathering. Section 5 gives experimental results which demonstrate the advantages of eliminating redundant sensing. We conclude with a discussion of related and future work.