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<h1> Agents and learning </h1>
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<ul>

<p><li> The Columbia <a
href="http://www.cs.columbia.edu/~sal/JAM/PROJECT">JAM Project</a> is
building an infrastructure for launching Java-based learning agents
over network-based information systems that then spawn learned
"classifier agents". These classifier agents migrate to other sites
and are combined by "meta-learning".  The resultant "meta-classfier
agents" can then migrate as well to harvest additional knowledge from
other agents.  The particular application under study involves fraud
and intrusion detection in financial information systems.  This work
is being done in collaboration with the Financial Services Technology
Consortium, a not-for-profit R&D organization whose members include
many of the nation's largest banks and associated vendor community.
<i>10/16/96</i>


<p><li> Workshop: <a
href="http://www.lab7.kuis.kyoto-u.ac.jp/icmas96-liome/"> Learning,
Interaction and Organizations in Multiagent Environments</a>, ICMAS'96
Workshop, December 10th, 1996.  Extended abstracts due August 25. <i>
7/8/96 </i>



<p><li> Call For Papers Journal of Experimental and Theoretical
Artificial Intelligence (JETAI) Special Issue on <a
href="http://www7.informatik.tu-muenchen.de/~weissg/si-jetai">
Learning in Distributed Artificial Intelligence Systems </a>.  Guest
Editor: Gerhard Weiss.  JETAI is an international journal published by
Taylor and Francis.  Editor-in-Chief: Eric Dietrich, State University of
New York, Binghamton.  Important Dates -- April 7, 1996, contact guest
editor; November 1, 1996, submission deadline.  <i> 6/16/96 </i>

<li><p> Call for papers -- Special Issue on <a
href="http://www7.informatik.tu-muenchen.de/~weissg/si-mlj">
Multiagent Learning </a> of the Journal Machine Learning.
Guest-edited by Michael Huhns and Gerhard Weiss.  "Multiagent
learning, that is, learning that relies on or even requires the
interaction between several computational agents, establishes a
relatively young but significant topic in artificial intelligence.
The goal of this special issue is to increase awareness of this topic,
and to serve as a basis for stimulating further research." <i> 6/16/96
</i>

<p><li> Thesis: Beerud Sheth, <a href=
"http://agents.www.media.mit.edu/groups/agents/papers/newt-thesis/main.html">
NEWT: A Learning Approach to Personalized Information Filtering</a>,
MIT Media Lab.  (also available as <a
href="ftp://media.mit.edu/pub/agents/interface-agents/news-filter.ps">
Postscript </a>), <i> 3/20/96</i>


<p><li> Book: Gerhard Weiss & Sandip Sen (Eds.), <a
href="http://www7.informatik.tu-muenchen.de/~weissg/LNAI-1042">
Adaption and Learning in Multi-Agent Systems</a>, Springer-Verlag,
1996. <i> 3/20/96 </i>


<p> <li> <a href="http://dis.cs.umass.edu/research/agents-learn.html">
Learning in Multi-agent Systems </a>


</ul>

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