Coordination of large numbers of agents to perform complex tasks in complex domains is a rapidly progressing area of research. Because of the high complexity of the problem, approximate and heuristic algorithms are typically used for key coordination tasks. Such algorithms usually require tuning algorithm parameters to yield the best performance under particular circumstances. Manually tuning parameters is sometimes difficult. In domains where characteristics of the environment can vary dramatically from scenario to scenario, it is desirable to have automated techniques for appropriately configuring the coordination. This research presents an approach to online reconfiguration of heuristic coordination algorithms. The approach uses an abstract simulation to produce a large performance data set to train a stochastic neural network that concisely models the complex, probabilistic relationship between configurations, environments and performance metrics. The final stochastic neural network, referred as the team performance model, is then used as the core of a tool that allows rapid online or offline configuration of coordination algorithms to particular scenarios and user preferences. The overall system allows rapid adaptation of coordination, leading to better performance in new scenarios. Results show that the team performance model captured key features of a very large configuration space and mostly captured the uncertainty in performance well. The tool was shown to be often capable of reconfiguring the algorithms to meet user requests for increases or decreases in performance parameters. This work represents the first practical approach to quickly reconfiguring a complex set of algorithms for a specific scenario.
| Identifer | oai:union.ndltd.org:PITT/oai:PITTETD:etd-12122007-164901 |
| Date | 31 January 2008 |
| Creators | Polvichai, Jumpol |
| Contributors | Dr. Paul Scerri, Professor Michael Lewis, Professor Paul Munro, Professor Stephen Hirtle, Professor Katia Sycara |
| Publisher | University of Pittsburgh |
| Source Sets | University of Pittsburgh |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.library.pitt.edu/ETD/available/etd-12122007-164901/ |
| Rights | unrestricted, I hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to University of Pittsburgh or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report. |
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