teamcore_people_alumni_phd_thesis_jonathan_thesis
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teamcore_people_alumni_phd_thesis_jonathan_thesis
LOCAL OPTIMIZATION IN COOPERATIVE AGENT NETWORKSbyJonathan p. PearceA Dissertation Presented to the FACULTY OF THE GRADUATE SCHOOL UNIVERSITY OF SOUTH teamcore_people_alumni_phd_thesis_jonathan_thesisHERN CALIFORNIAIn Partial Fulfillment of the Requirements for the Degree DOCTOR OF PHILOSOPHY (COMPUTER SCIENCE)39234Copyright 2007Jonathan p. PearceAcknowledgmentsIwould like to thank my advisor. Milind Tambc, for his constant support and encouragement in all aspects of my research and career. I al teamcore_people_alumni_phd_thesis_jonathan_thesisso thank my collaborator. Rajiv Maheswaran, for helping to guide me in my first two years at use and for many helpful discussions and ideas about Ẳ-opteamcore_people_alumni_phd_thesis_jonathan_thesis
timality. I thank Victor Lesser. Fernando Ordonez, Sven Koenig, and Sarit Kraus, both for their insightful thoughts and discussions on the work in thiLOCAL OPTIMIZATION IN COOPERATIVE AGENT NETWORKSbyJonathan p. PearceA Dissertation Presented to the FACULTY OF THE GRADUATE SCHOOL UNIVERSITY OF SOUTH teamcore_people_alumni_phd_thesis_jonathan_thesisng graduate school. Thanks also go to Gal Kaminka. Bhaskar Krishnamachari. Paul Sccrri. and Makoto Yokoo for helpful insights on the material in this thesis. I also thank Amos Freedy and Gershon Weltman of Perceptronics Solutions. Inc. very much for their help in supporting this work and obtaining r teamcore_people_alumni_phd_thesis_jonathan_thesiseal-world data.I also thank members and alumni of the TEAMCORE research group for their support and for being good friends: Emma Bowring. Tapana Guptateamcore_people_alumni_phd_thesis_jonathan_thesis
. I lyuckchul Jung. Janusz Marecki. Ranjit Nair. Praveen Paruchuri. Nathan Schurr. Zvi Topol, and Pradeep Varakantham. Additional thanks are due to ZvLOCAL OPTIMIZATION IN COOPERATIVE AGENT NETWORKSbyJonathan p. PearceA Dissertation Presented to the FACULTY OF THE GRADUATE SCHOOL UNIVERSITY OF SOUTH teamcore_people_alumni_phd_thesis_jonathan_thesisnd finally. I thank my wife Liang for all the help and support she has given me during this time.iiAbstractMy research focuses on constructing and analyzing systems of intelligent, autonomous agents, lltesc agents may include people, physical robots, or software programs acting as assistants, teamma teamcore_people_alumni_phd_thesis_jonathan_thesistes, opponents, or trading partners. In a large class of multi-agent scenarios, the effect of local interactions between agents can be compactly repreteamcore_people_alumni_phd_thesis_jonathan_thesis
sented as a network structure such as a distributed constraint optimization problem (DCOP) for cooperative domains. Collaboration between large groupsLOCAL OPTIMIZATION IN COOPERATIVE AGENT NETWORKSbyJonathan p. PearceA Dissertation Presented to the FACULTY OF THE GRADUATE SCHOOL UNIVERSITY OF SOUTH teamcore_people_alumni_phd_thesis_jonathan_thesisorder to find a workable solution in a timely manner. The goal of my thesis is to provide algorithms to enable networks of agents that arc bounded in this way to quickly find high-quality solutions, as well as theoretical results to understand key properties of these solutions. Relevant domains for teamcore_people_alumni_phd_thesis_jonathan_thesismy work include personal assistant agents, sensor networks, and teams of autonomous robots.In particular, this thesis considers the case in which agenteamcore_people_alumni_phd_thesis_jonathan_thesis
ts optimize a DCOP by forming groups of one or more agents until no group of k or fewer agents can possibly improve the solu Cion: we define this typeLOCAL OPTIMIZATION IN COOPERATIVE AGENT NETWORKSbyJonathan p. PearceA Dissertation Presented to the FACULTY OF THE GRADUATE SCHOOL UNIVERSITY OF SOUTH teamcore_people_alumni_phd_thesis_jonathan_thesisd to k optimality. The first set of results arc worst case guarantees on the solution quality of A: optima in a DCOP.iiihttps://khothuvien.cori!These guarantees can help determine an appropriate A-optimal algorithm, or possibly an appropriate constraint graph structure, for agents to use in situatio teamcore_people_alumni_phd_thesis_jonathan_thesisns where the cost of coordination between agents must be weighed against the quality of the solution reached. The second set of results arc upper bounteamcore_people_alumni_phd_thesis_jonathan_thesis
ds on the number of A-optima that can exist in a DCOP. Because each joint action consumes resources, knowing the maximal number of A-optimal joint actLOCAL OPTIMIZATION IN COOPERATIVE AGENT NETWORKSbyJonathan p. PearceA Dissertation Presented to the FACULTY OF THE GRADUATE SCHOOL UNIVERSITY OF SOUTH teamcore_people_alumni_phd_thesis_jonathan_thesisevel of A-optimality. given fixed resource. The third contribution is a set of 2-optimal and 3-optimal algorithms and an experimental analysis of the performance of I-. 2-. and 3-optimal algorithms on several types of DCOPs. The final contribution of this thesis is a ease study of the application of teamcore_people_alumni_phd_thesis_jonathan_thesis A-optimal DCOP algorithms and solutions to the problem of the formation of human teams spanning multiple organizations. Given a particular specificatteamcore_people_alumni_phd_thesis_jonathan_thesis
ion of a human team (such as a task force to respond to an emergency) and a pool of possible team members, a DCOP can be formulated to match this specLOCAL OPTIMIZATION IN COOPERATIVE AGENT NETWORKSbyJonathan p. PearceA Dissertation Presented to the FACULTY OF THE GRADUATE SCHOOL UNIVERSITY OF SOUTH teamcore_people_alumni_phd_thesis_jonathan_thesiseam that will be used.ivContentsAcknowledgmentsiiAbstractiiiList Of FiguresvllList Of TableslxList Of AlgorithmsX1Introduction and RelatedWork11.1Introduction............................................................... 11.2Related Work.............................................................. teamcore_people_alumni_phd_thesis_jonathan_thesis. 51.2.1Constraint Reasoning............................................... 51.2.2Distributed Markos Decision Processes..............................teamcore_people_alumni_phd_thesis_jonathan_thesis
71.2.3Local Search and Gaine Theory...................................... 71.2.4Local Search in Combinatorial Optimization......................... 81LOCAL OPTIMIZATION IN COOPERATIVE AGENT NETWORKSbyJonathan p. PearceA Dissertation Presented to the FACULTY OF THE GRADUATE SCHOOL UNIVERSITY OF SOUTHLOCAL OPTIMIZATION IN COOPERATIVE AGENT NETWORKSbyJonathan p. PearceA Dissertation Presented to the FACULTY OF THE GRADUATE SCHOOL UNIVERSITY OF SOUTHGọi ngay
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