Efficient Approximate Inference for Online Probabilistic Plan Recognition

Contents

We present a new general framework for online probabilistic plan recognition called the Abstract Hidden Markov Memory Model (AHM�M). The new model is an extension of the existing Abstract Hidden Markov Model to allow the policy to have internal memory which can be updated in a Markov fashion. We show that the AHM�M can represent a richer class of probabilistic plans, and at the same time derive an ef- ficient algorithm for plan recognition in the AHM�M based on the Rao-Blackwellised Particle Filter approximate inference method.