Full metadata
Title
Multiagent Optimization Problems: Bridging Practicality and Predictability
Description
This dissertation is an examination of collective systems of computationally limited agents that require coordination to achieve complex ensemble behaviors or goals. The design of coordination strategies can be framed as multiagent optimization problems, which are addressed in this work from both theoretical and practical perspectives. The primary foci of this study are models where computation is distributed over the agents themselves, which are assumed to possess onboard computational capabilities. There exist many assumption variants for distributed models, including fairness and concurrency properties. In general, there is a fundamental trade-off whereby weakening model assumptions increases the applicability of proposed solutions, while also increasing the difficulty of proving theoretical guarantees. This dissertation aims to produce a deeper understanding of this trade-off with respect to multiagent optimization and scalability in distributed settings. This study considers four multiagent optimization problems. The model assumptions begin with fully centralized computation for the all-or-nothing multicommodity flow problem, then progress to synchronous distributed models through examination of the unmapped multivehicle routing problem and the distributed target localization problem. The final model is again distributed but assumes an unfair asynchronous adversary in the context of the energy distribution problem for programmable matter. For these problems, a variety of algorithms are presented, each of which is grounded in a theoretical foundation that permits formal guarantees regarding correctness, running time, and other critical properties. These guarantees are then validated with in silico simulations and (in some cases) physical experiments, demonstrating empirically that they may carry over to the real world. Hence, this dissertation bridges a portion of the predictability-practicality gap with respect to multiagent optimization problems.
Date Created
2023
Contributors
- Weber, Jamison Wayne (Author)
- Richa, Andréa W (Thesis advisor)
- Bertsekas, Dimitri P (Committee member)
- Murphey, Todd D (Committee member)
- Jiang, Zilin (Committee member)
- Arizona State University (Publisher)
Topical Subject
Resource Type
Extent
207 pages
Language
eng
Copyright Statement
In Copyright
Primary Member of
Peer-reviewed
No
Open Access
No
Handle
https://hdl.handle.net/2286/R.2.N.190995
Level of coding
minimal
Cataloging Standards
Note
Partial requirement for: Ph.D., Arizona State University, 2023
Field of study: Computer Science
System Created
- 2023-12-14 02:07:21
System Modified
- 2023-12-14 02:07:28
- 11 months 1 week ago
Additional Formats