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Planning in Hybrid Structured Stochastic Domains

Kveton, Branislav (2007) Planning in Hybrid Structured Stochastic Domains. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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Efficient representations and solutions for large structured decision problems with continuous and discrete variables are among the important challenges faced by the designers of automated decision support systems. In this work, we describe a novel hybrid factored Markov decision process (MDP) model that allows for a compact representation of these problems, and a hybrid approximate linear programming (HALP) framework that permits their efficient solutions. The central idea of HALP is to approximate the optimal value function of an MDP by a linear combination of basis functions and optimize its weights by linear programming. We study both theoretical and practical aspects of this approach, and demonstrate its scale-up potential on several hybrid optimization problems.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairHauskrecht, Milosmilos@cs.pitt.eduMILOS
Committee MemberGuestrin,
Committee MemberCooper, Gregorygfc@cbmi.pitt.eduGFC
Committee Memberzdzel, Marekmarek@sis.pitt.eduDRUZDZEL
Date: 30 January 2007
Date Type: Completion
Defense Date: 7 September 2006
Approval Date: 30 January 2007
Submission Date: 28 November 2006
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Institution: University of Pittsburgh
Schools and Programs: Dietrich School of Arts and Sciences > Intelligent Systems
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: Bayesian networks; graphical models; planning under uncertainty; sequential decision making under uncertainty; decision theory; Markov decision processes
Other ID:, etd-11282006-142547
Date Deposited: 10 Nov 2011 20:06
Last Modified: 15 Nov 2016 13:52


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