Multi-Agent Learning and Adaptation in an Information Filtering Market

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TypeArticle
ConferenceProceedings of the AAAI Spring Symposium on Adaptation, Co-evolution and Learning in Multiagent Systems, March 25-27, 1996., Stanford University, California, USA
AbstractThis paper presents an adaptive model for multi-agent coordination based on the metaphor of economic markets. This model has been used to develop SIGMA, a system for filtering Usenet Netnews which is able to cope with the non-stationary and partially observable nature of the information filtering task at hand. SIGMA integrates a number of different learning and adaptation techniques, including reinforcement learning, bidding price adjustment, and relevance feedback. Aspects of these are discussed below.
Publication date
LanguageEnglish
AffiliationNRC Institute for Information Technology; National Research Council Canada
Peer reviewedNo
NRC number39186
NPARC number8914416
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Record identifier2ccd226c-fe2d-4f63-a488-cbc3ec0e8b45
Record created2009-04-22
Record modified2016-05-09
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