A General Model for Online Probabilistic Plan Recognition

We present a new general framework for online istic plan recognition called the Abstract Hidden Markov Memory Model (AHMEM). The l 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 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 RaoBlackwellised Particle Filter approximate inference method.

• Type: Conference
• Year: 2003
• Where: IJCAI
• Authors: Hung Hai Bui
• Keywords: Abstract Hidden Markov, Hidden Markov Memory, Hidden Markov Model, IJCAI 2003, IJCAI 2007