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Robust Agent Communities.

Authors :
Carbonell, Jaime G.
Siekmann, Jörg
Gorodetsky, Vladimir
Zhang, Chengqi
Skormin, Victor A.
Cao, Longbing
Sen, Sandip
Saha, Sabyasachi
Airiau, Stéphane
Candale, Teddy
Banerjee, Dipyaman
Chakraborty, Doran
Mukherjee, Partha
Gursel, Anil
Source :
Autonomous Intelligent Systems: Multi-Agents & Data Mining; 2007, p28-45, 18p
Publication Year :
2007

Abstract

We believe that intelligent information agents will represent their users interest in electronic marketplaces and other forums to trade, exchange, share, identify, and locate goods and services. Such information worlds will present unforeseen opportunities as well as challenges that can be best addressed by robust, self-sustaining agent communities. An agent community is a stable, adaptive group of self-interested agents that share common resources and must coordinate their efforts to effectively develop, utilize and nurture group resources and organization. More specifically, agents will need mechanisms to benefit from complementary expertise in the group, pool together resources to meet new demands and exploit transient opportunities, negotiate fair settlements, develop norms to facilitate coordination, exchange help and transfer knowledge between peers, secure the community against intruders, and learn to collaborate effectively. In this talk, I will summarize some of our research results on trust-based computing, negotiation, and learning that will enable intelligent agents to develop and sustain robust, adaptive, and successful agent communities. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISBNs :
9783540728382
Database :
Supplemental Index
Journal :
Autonomous Intelligent Systems: Multi-Agents & Data Mining
Publication Type :
Book
Accession number :
33213868
Full Text :
https://doi.org/10.1007/978-3-540-72839-9_3