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BALANCING LOCAL RESOURCES AND GLOBAL GOALS IN
MULTIPLY-CONSTRAINED DISTRIBUTED CONSTRAINT OPTIMIZATION
by
Emma Bowring
A Dissertation Presented to the
FACULTY OF THE GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
MASTER OF SCIENCE
(COMPUTER SCIENCE)
December 2007
Copyright 2007 Emma Bowring
Object Description
| Title | Balancing local resources and global goals in multiply-constrained distributed constraint optimization |
| Author | Bowring, Emma |
| Author email | bowring@usc.edu |
| Degree | Doctor of Philosophy |
| Document type | Dissertation |
| Degree program | Computer Science |
| School | Viterbi School of Engineering |
| Date defended/completed | 2007-07-02 |
| Date submitted | 2007 |
| Restricted until | Unrestricted |
| Date published | 2007-10-23 |
| Advisor (committee chair) | Tambe, Milind |
| Advisor (committee member) |
Grosz, Barbara Gil, Yolanda Marsella, Stacy Balsamo, Anne |
| Abstract | Distributed constraint optimization (DCOP) is a useful framework for cooperative multiagent coordination. DCOP focuses on optimizing a single team objective. However, in many domains, agents must satisfy constraints on resources consumed locally while optimizing the team goal. These resource constraints may need to be kept private or shared to improve efficiency. Extending DCOP to these domains raises two issues: algorithm design and sensitivity analysis. Algorithm design requires creating algorithms that trade off completeness, scalability, privacy and efficiency. Sensitivity analysis examines whether slightly increasing the available resources could yield a significantly better outcome.; This thesis defines the multiply-constrained DCOP (MC-DCOP) framework and provides complete and incomplete algorithms for solving MC-DCOP problems. Complete algorithms find the best allocation of scarce resources, while incomplete algorithms are more scalable. The algorithms use mutually-intervening search; they use local resource constraints to intervene in the search for the globally optimal solution. The algorithms use four key techniques: (i) transforming constraints to maintain privacy; (ii) dynamically setting upper bounds on resource consumption; (iii) identifying the extent to which the local graph structure allows agents to compute exact bounds on resource consumption; and (iv) using a virtual assignment to flag problems rendered unsatisfiable by their resource constraints. Proofs of correctness are presented for all algorithms. Finally, the complete and incomplete algorithms are used in conjunction with one another to perform distributed local reoptimization to address sensitivity analysis.; Experimental results demonstrated that MC-DCOP problems are most challenging when resources are scarce but sufficient. In problems where there are insufficient resources, the team goal is largely irrelevant. In problems with ample resources, the local resource constraints require little consideration. The incomplete algorithms were two orders of magnitude more efficient than the complete algorithm for the most challenging MC-DCOP problems and their runtime increased very little as the number of agents in the network increased. Finally, sensitivity analysis results indicated that local reoptimization is an effective way to identify resource constraints that are creating bottlenecks. Taken together these new algorithms and examination of the problem of sensitivity analysis help extend the applicability of DCOP to more complex domains. |
| Keyword | multiagent systems |
| Language | English |
| Part of collection | University of Southern California dissertations and theses |
| Publisher (of the original version) | University of Southern California |
| Place of publication (of the original version) | Los Angeles, California |
| Publisher (of the digital version) | University of Southern California. Libraries |
| Type | texts |
| Legacy record ID | usctheses-m883 |
| Rights | Bowring, Emma |
| Repository name | Libraries, University of Southern California |
| Repository address | Los Angeles, California |
| Repository email | http://www.usc.edu/isd/libraries/services/ask_a_librarian/email/ |
| Filename | etd-Bowring-20071023 |
| Archival file | uscthesesreloadpub_Volume35/etd-Bowring-20071023.pdf |
Description
| Title | Page 1 |
| Full text | BALANCING LOCAL RESOURCES AND GLOBAL GOALS IN MULTIPLY-CONSTRAINED DISTRIBUTED CONSTRAINT OPTIMIZATION by Emma Bowring A Dissertation Presented to the FACULTY OF THE GRADUATE SCHOOL UNIVERSITY OF SOUTHERN CALIFORNIA In Partial Fulfillment of the Requirements for the Degree MASTER OF SCIENCE (COMPUTER SCIENCE) December 2007 Copyright 2007 Emma Bowring |
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