NDLI: Leveraging Organizational Guidance Policies with Learning to Self-Tune Multiagent Systems

Content Provider	IEEE Xplore Digital Library
Author	Harmon, S.J. DeLoach, S.A. Robby Caragea, D.
Copyright Year	2008
Description	Author affiliation: Multiagent & Cooperative Robot. Lab., Kansas State Univ., Manhattan, KS (Harmon, S.J.; DeLoach, S.A.; Robby; Caragea, D.)
Abstract	As organization-based multiagent systems are applied to more complex problems, configuring and tuning the systems can become nearly as complex as the original problem a system was designed to solve. A robust system should be able to adapt. It should be able to self-configure and self-tune. To this end, we propose a method for self-tuning using the concept of guidance policies, that is policies that are designed to guide the system without sacrificing its flexibility. Guidance policies allow us to apply traditional learning techniques online without many of the drawbacks associated with a system falling into a local optimum. They also help simplify the learning process. We examine the impact of this learning on various multiagent systems.
Starting Page	223
Ending Page	232
File Size	544423
Page Count	10
File Format	PDF
ISBN	9780769534046
DOI	10.1109/SASO.2008.46
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-10-20
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Policy Multiagent systems Laboratories Humans Reinforcement Learning Modeling Tuning Learning systems Design engineering Robustness Multiagent Systems Self-Tuning Robots
Content Type	Text
Resource Type	Article

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