A general model for online probabilistic plan recognition
Hung Bui · 2003
We present a new general framework for online Hidden Markov Memory Model (AHMEM). The Hidden Markov Model to allow the policy to have internal memory which can be updated in a Markov fashion. We show that the AHMEM can repre-sent a richer class of probabilistic plans, and at the same time derive an efficient algorithm for plan recognition in the AHMEM based on the Rao-Blackwellised Particle Filter approximate inference method. 1