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. Attached Files Name Description MIMEType Size Downloads Title On the recognition of abstract Markov policies Author(s) Bui, Hung H. Venkatesh, Svetha orcid.org/0000-0001-8675-6631 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 Related Links Link Description Connect to published version (restricted access) Go to link with your DU access privileges     Link to Related Work On the recognition of abstract Markov policies (deposited 09-10-2017) AAAI Press Publisher (deposited 06-11-2013) Association for the Advancement of Artificial Intelligence (AAAI) Publisher (deposited 08-05-2012)   Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Tue, 01 May 2012, 10:58:10 EST Thu, 03 May 2012, 11:12:06 EST Thu, 03 May 2012, 11:14:07 EST Mon, 14 May 2012, 09:40:58 EST Wed, 15 Jan 2014, 12:57:25 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 240 Abstract Views, 3 File Downloads  -  Detailed Statistics Created: Tue, 01 May 2012, 10:58:10 EST

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