SIGMA: Integrating Learning Techniques in Computational Markets for Information Filtering

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TypeArticle
ConferenceProceedings of the AAAI Spring Symposium on Machine Learning in Information Access, March 25-27, 1996., Stanford, California, USA
AbstractThis paper presents an adaptive model for multi-agent learning based on the metaphor of economic markets that can cope with the non-stationary and partially observable nature of an information filtering task. Various learning and adaptation techniques - i.e. reinforcement learning, bidding price adjustment and relevance feedback - are integrated into the model. As a result of this integration learning through the model exploits market competition in order to dynamically construct mixtures of 'local experts' from selfish agents. The model is embedded into SIGMA (System of Information Gathering Market-based Agents) for information filtering of Usenet netnews. The functionality of the system is discussed together with work underway for its evaluation.
Publication date
LanguageEnglish
AffiliationNRC Institute for Information Technology; National Research Council Canada
Peer reviewedNo
NRC number39187
NPARC number8913096
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Record identifier3164742f-beec-48d4-a8ee-207e271403e3
Record created2009-04-22
Record modified2016-05-09
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