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On the recognition of abstract Markov policies
conference contribution
posted on 2000-01-01, 00:00 authored by H Bui, Svetha VenkateshSvetha Venkatesh, G WestAbstraction plays an essential role in the way the agents plan their behaviours, especially to reduce the computational complexity of planning in large domains. However, the effects of abstraction in the inverse process – plan recognition – are unclear. In this paper, we present a method for recognising the agent’s behaviour in noisy and uncertain domains, and across multiple levels of abstraction. We use the concept of abstract Markov policies in abstract probabilistic planning as the model of the agent’s behaviours and employ probabilistic inference in Dynamic Bayesian Networks (DBN) to infer the correct policy from a sequence of observations. When the states are fully observable, we show that for a broad and often-used class of abstract policies, the complexity of policy recognition scales well with the number of abstraction levels in the policy hierarchy. For the partially observable case, we derive an efficient hybrid inference scheme on the corresponding DBN to overcome the exponential complexity.
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Event
National Conference on Artificial Intelligence (17th : 2000 : Austin, Tex.)Pagination
524 - 530Publisher
AAAI PressLocation
Austin, Tex.Place of publication
Cambridge, Mass.Start date
2000-07-30End date
2000-08-03ISBN-13
9780262511124ISBN-10
0262511126Language
engPublication classification
E1.1 Full written paper - refereedCopyright notice
2000, AAAITitle of proceedings
AAAI-2000 : Proceedings of the 17th National Conference on Artificial IntelligenceUsage metrics
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