Efficient Approximate Inference for Online Probabilistic Plan Recognition

Hung H. Bui

We present a new general framework for online probabilistic plan recognition called the Abstract Hidden Markov Memory Model (AHMM). 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 AHMM can represent a richer class of probabilistic plans, and at the same time derive an ef- ficient algorithm for plan recognition in the AHMM based on the Rao-Blackwellised Particle Filter approximate inference method.

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