Goal recognition over POMDPs: inferring the intention of a POMDP agent

Plan recognition is the problem of inferring the goals and plans of an agent from partial observations of her behavior. Recently, it has been shown that the problem can be formulated and solved usingplanners, reducing plan recognition to plan generation.In this work, we extend this model-basedapproach to plan recognition to the POMDP setting, where actions are stochastic and states are partially observable. The task is to infer a probability distribution over the possible goals of an agent whose behavior results from a POMDP model. The POMDP model is shared between agent and observer except for the true goal of the agent that is hidden to the observer. The observations are action sequences O that may contain gaps as some or even most of the actions done by the agent may not be observed. We show that the posterior goal distribution P(GjO) can be computed from the value function VG(b) over beliefs b generated by the POMDPplanner for each possible goal G. Some extensionsof the basic framework are discussed, and a numberof experiments are reported.

Computing infinite plans for LTL goals using a classical planner

Classical planning has been notably successful in synthesizing finite plans to achieve states where propositional goals hold. In the last few years, classical planning has also been extended to incorporate temporally extended goals, expressed in temporal logics such as LTL, to impose restrictions on the state sequences generated by finite plans. In this work, we take the next step and consider the computation of infinite plans for achieving arbitrary LTL goals. We show that infinite plans can also be obtained efficiently by calling a classical planner once over a classical planning encoding that represents and extends the composition of the planningdomain and the B¨uchi automaton representingthe goal. This compilation scheme has been implemented and a number of experiments are reported.