Learning, Information Exchange, and Joint-Deliberation through Argumentation in Multi-agent Systems

Authors:
Santi Ontañón;Enric Plaza
Affiliations:
CCL, Cognitive Computing Lab Georgia Institute of Technology, Atlanta, GA 303322/0280;IIIA, Artificial Intelligence Research Institute - CSIC, Spanish Council for Scientific Research, Bellaterra, Spain 08193
Venue:
OTM '08 Proceedings of the OTM Confederated International Workshops and Posters on On the Move to Meaningful Internet Systems: 2008 Workshops: ADI, AWeSoMe, COMBEK, EI2N, IWSSA, MONET, OnToContent + QSI, ORM, PerSys, RDDS, SEMELS, and SWWS
Year:
2008

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Abstract

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 ($\mathcal{M}{\normalfont \textsf{AC}}$ systems), outline our argumentation framework and provide several examples of new tasks that agents in a $\mathcal{M}\normalfont \textsf{AC}$ system can undertake thanks to the argumentation processes.