Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1613/jair.839 | Journal of Artificial Intelligence Research |
Keywords | DocType | Volume |
plan execution model,novel plan recognition framework,plan recognition,abstract hidden markov model,plan hierarchy,stochastic process,plan execution,problem on-line plan recognition,online probabilistic plan recognition,efficient plan recognition algorithm,probabilistic inference,policy recognition,network model,decision theory,mathematical models,neural networks,dynamic bayesian network,probability,markov processes,stochastic model,computational complexity,artificial intelligent | Journal | abs/1106.0672 |
Issue | ISSN | Citations |
1 | Journal Of Artificial Intelligence Research, Volume 17, pages
451-499, 2002 | 108 |
PageRank | References | Authors |
6.60 | 27 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hung Hai Bui | 1 | 1188 | 112.37 |
| Svetha Venkatesh | 2 | 4190 | 425.27 |
| Geoff A. W. West | 3 | 108 | 6.60 |