Planning with State Uncertainty via Contingency Planning and Execution Monitoring

This paper proposes a fast alternative to POMDP planning for domains with deterministic state-changing actions but probabilistic observation-making actions and partial observability of the initial state. This class of planning problems, which we call quasi-deterministic problems, includes important realworld domains such as planning for Mars rovers. The approach we take is to reformulate the quasi-deterministic problem into a completely observable problem and build a contingency plan where branches in the plan occur wherever an observational action is used to determine the value of some state variable. The plan for the completely observable problem is constructed assuming that state variables can be determined exactly at execution time using these observational actions. Since this is often not the case due to imperfect sensing of the world, we then use execution monitoring to select additional actions at execution time to determine the value of the state variable sufficiently accurately. We use a value of information calculation to determine which information gathering actions to perform and when to stop gathering information and continue execution of the branching plan. We show empirically that while the plans found are not optimal, they can be generated much faster, and are of better quality than other approaches.