An argumentation framework for learning, information exchange, and joint-deliberation in multi-agent systems1 | Zendy

Santiago Ontañón | Zendy; Enric Plaza | Zendy

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An argumentation framework for learning, information exchange, and joint-deliberation in multi-agent systems1

Author(s) -

Santiago Ontañón,

Enric Plaza

Publication year - 2011

Publication title -

multiagent and grid systems

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.181

H-Index - 19

eISSN - 1875-9076

pISSN - 1574-1702

DOI - 10.3233/mgs-2011-0169

Subject(s) - argumentation theory , deliberation , computer science , joint (building) , multi agent system , artificial intelligence , information exchange , knowledge management , management science , epistemology , telecommunications , architectural engineering , philosophy , politics , political science , law , economics , engineering

Case-Based Reasoning (CBR) can give agents the capability of learning from their own experience and solve new problems, however, in a multi-agent system, the ability of agents to collaborate is also crucial. In this paper we present an argumentation framework (AMAL) designed to provide learning agents with collaborative problem solving (joint deliberation) and information sharing capabilities (learning from communication). We will introduce the idea of CBR multi-agent systems (MAC systems), outline our argumentation framework and provide several examples of new tasks that agents in a MAC system can undertake thanks to the argumentation processes. © 2011 - IOS Press and the authors. All rights reserved.Peer Reviewe

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