Error-Tolerant Anytime Approach to Plan Recognition Using a Particle Filter
Classical plan recognition approaches require restrictive assumptions and are generally off-line. However, many real-world plan recognition applications must deal with real-time constraints, noisy information, temporal relations in actions, agent preferences, and so on. Many existing approaches have tried to relax assumptions, but none can deal with the above-cited needs. This paper proposes an extension of previous works on plan recognition based on plan tree grammar. Our anytime topdown approach uses a particle filter. This approach manages to give a quick reliable solution to the plan recognition problem while dealing with noisy observations and without reducing the expressiveness of plan libraries. Empirical results on simulated problems show the efficiency of our approach.