Multi-Agent Learning and Adaptation in an Information Filtering Market

Download	View accepted manuscript: Multi-Agent Learning and Adaptation in an Information Filtering Market (PDF, 487 KiB)
Author	Search for: Ferguson, I.A.; Search for: Karakoulas, G.J.
Format	Text, Article
Conference	Proceedings of the AAAI Spring Symposium on Adaptation, Co-evolution and Learning in Multiagent Systems, March 25-27, 1996., Stanford University, California, USA
Abstract	This 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	1996
Language	English
NRC number	NRCC 39186
NPARC number	8914416
Export citation	Export as RIS
Report a correction	Report a correction (opens in a new tab)
Record identifier	2ccd226c-fe2d-4f63-a488-cbc3ec0e8b45
Record created	2009-04-22
Record modified	2020-03-20