Kveton, Branislav
(2007)
Planning in Hybrid Structured Stochastic Domains.
Doctoral Dissertation, University of Pittsburgh.
(Unpublished)
Abstract
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.
Share
Citation/Export: |
|
Social Networking: |
|
Details
Item Type: |
University of Pittsburgh ETD
|
Status: |
Unpublished |
Creators/Authors: |
|
ETD Committee: |
|
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: |
http://etd.library.pitt.edu/ETD/available/etd-11282006-142547/, etd-11282006-142547 |
Date Deposited: |
10 Nov 2011 20:06 |
Last Modified: |
15 Nov 2016 13:52 |
URI: |
http://d-scholarship.pitt.edu/id/eprint/9828 |
Metrics
Monthly Views for the past 3 years
Plum Analytics
Actions (login required)
|
View Item |