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Planning in Hybrid Structured Stochastic Domains

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.

Identiferoai:union.ndltd.org:PITT/oai:PITTETD:etd-11282006-142547
Date30 January 2007
CreatorsKveton, Branislav
ContributorsCarlos Guestrin, Gregory Cooper, Marek Druzdzel, Milos Hauskrecht
PublisherUniversity of Pittsburgh
Source SetsUniversity of Pittsburgh
LanguageEnglish
Detected LanguageEnglish
Typetext
Formatapplication/pdf
Sourcehttp://etd.library.pitt.edu/ETD/available/etd-11282006-142547/
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