On the recognition of abstract Markov policies

Bui, Hung H., Venkatesh, Svetha and West, Geoff 2000, On the recognition of abstract Markov policies, in AAAI-2000 : Proceedings of the 17th National Conference on Artificial Intelligence, AAAI Press, Cambridge, Mass., pp. 524-530.

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Title On the recognition of abstract Markov policies
Author(s) Bui, Hung H.
Venkatesh, Svetha
West, Geoff
Conference name National Conference on Artificial Intelligence (17th : 2000 : Austin, Tex.)
Conference location Austin, Tex.
Conference dates 30 July-3 Aug. 2000
Title of proceedings AAAI-2000 : Proceedings of the 17th National Conference on Artificial Intelligence
Editor(s) [Unknown]
Publication date 2000
Conference series National Conference on Artificial Intelligence
Start page 524
End page 530
Total pages 7
Publisher AAAI Press
Place of publication Cambridge, Mass.
Keyword(s) abstraction
agents
abstract Markov policies
dynamic Bayesian networks (DBN)
Summary Abstraction 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.
ISBN 9780262511124
0262511126
Language eng
Field of Research 089999 Information and Computing Sciences not elsewhere classified
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1.1 Full written paper - refereed
Copyright notice ©2000, AAAI
Persistent URL http://hdl.handle.net/10536/DRO/DU:30044789

Document type: Conference Paper
Collection: School of Information Technology
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