Decision diagrams as plans: Answering observation-grounded queries

Abstract

We consider a robot that answers questions about its environment by traveling to appropriate places and then sensing. Questions are posed as structured queries and may involve conditional or contingent relationships between observable properties. After formulating this problem, and empha-sizing the advantages of exploiting deducible information, we describe how non-trivial knowledge of the world and queries can be given a convenient, concise, unified representation via reduced ordered binary decision diagrams (BDDs). To use these data structures directly for inference and planning, we introduce a new product operation, and generalize the classic dynamic variable reordering techniques to solve planning problems. Also, finally, we evaluate optimizations that exploit locality.