Policy Recognition in the Abstract Hidden Markov Model
  In this paper, we present a method for recognising an agent's behaviour in
dynamic, noisy, uncertain domains, and across multiple levels of abstraction.
We term this problem on-line plan recognition under uncertainty and view it
generally as probabilistic inference on the stochastic process representing the
execution of the agent's plan. Our contributions in this paper are twofold. In
terms of probabilistic inference, we introduce the Abstract Hidden Markov Model
(AHMM), a novel type of stochastic processes, provide its dynamic Bayesian
network (DBN) structure and analyse the properties of this network. We then
describe an application of the Rao-Blackwellised Particle Filter to the AHMM
which allows us to construct an efficient, hybrid inference method for this
model. In terms of plan recognition, we propose a novel plan recognition
framework based on the AHMM as the plan execution model. The Rao-Blackwellised
hybrid inference for AHMM can take advantage of the independence properties
inherent in a model of plan execution, leading to an algorithm for online
probabilistic plan recognition that scales well with the number of levels in
the plan hierarchy. This illustrates that while stochastic models for plan
execution can be complex, they exhibit special structures which, if exploited,
can lead to efficient plan recognition algorithms. We demonstrate the
usefulness of the AHMM framework via a behaviour recognition system in a
complex spatial environment using distributed video surveillance data.
