Computing Contingent Plans Using Online Replanning (original) (raw)

In contingent planning under partial observability with sensing actions, agents actively use sensing to discover meaningful facts about the world. For this class of problems the solution can be represented as a plan tree, branching on various possible observations. Recent successful approaches translate the partially observable contingent problem into a non-deterministic fully observable problem, and then use a planner for non-deterministic planning. While this approach has been successful in many domains, the translation may become very large, encumbering the task of the non-deterministic planner. In this paper we suggest a different approach - using an online contingent solver repeatedly to construct a plan tree. We execute the plan returned by the online solver until the next observation action, and then branch on the possible observed values, and replan for every branch independently. In many cases a plan tree can be exponential in the number of state variables, but still, the t...