An approach to online optimization of heuristic coordination algorithms
Conference proceedings article
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Publication Details
Author list: Polvichai J., Scerri P., Lewis M.
Publisher: International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)
Publication year: 2008
Volume number: 2
Start page: 614
End page: 621
Number of pages: 8
ISBN: 9781605604701
ISSN: 1548-8403
eISSN: 1548-8403
Languages: English-Great Britain (EN-GB)
Abstract
Due to computational intractability, large scale coordination algorithms are necessarily heuristic and hence require tuning for particular environments. In domains where characteristics of the environment vary dramatically from scenario to scenario, it is desirable to have automated techniques for appropriately configuring the coordination. This paper presents an approach that takes performance data from a simulator to train a stochastic neural network that concisely models the complex, probabilistic relationship between configurations, environments and performance metrics. The stochastic neural network is 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. Copyright ฉ 2008, International Foundation for Autonomous Agents and Multi-agent Systems (www.ifaamas.org). All rights reserved.
Keywords
Complex system modeling, Large scale coordination algorithms
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