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From: jair-ed@ptolemy.arc.nasa.gov
Subject: New Article, Adaptive Load Balancing...
Message-ID: <1995May15.211609.11556@ptolemy-ethernet.arc.nasa.gov>
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Date: Mon, 15 May 1995 21:16:09 GMT
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JAIR is pleased to announce the publication of the following article:

Schaerf, A., Shoham, Y. and Tennenholtz, M. (1995)
  "Adaptive Load Balancing: A Study in Multi-Agent Learning", 
   Volume 2, pages 475-500.
   postscript: volume2/schaerf95a.ps (265K)
	       compressed, volume2/schaerf95a.ps.Z (107K)

   Abstract: We study the process of multi-agent reinforcement learning
   in the context of load balancing in a distributed system, without use
   of either central coordination or explicit communication.  We first
   define a precise framework in which to study adaptive load balancing,
   important features of which are its stochastic nature and the purely
   local information available to individual agents.  Given this
   framework, we show illuminating results on the interplay between basic
   adaptive behavior parameters and their effect on system efficiency.
   We then investigate the properties of adaptive load balancing in
   heterogeneous populations, and address the issue of exploration vs.
   exploitation in that context.  Finally, we show that naive use of
   communication may not improve, and might even harm system efficiency.


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