Hung H. Bui
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 exisng 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 recognitn in the AHM M based on the Rao-Blackwellised Particle Filter approximate inference method.