Affiliations: Artificial Intelligence Research Institute (IIIA-CSIC), Campus UAB, 08193 Bellaterra, Catalonia, Spain. E-mail: santi@iiia.csic.es, enric@iiia.csic.es
Correspondence:
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Part of this work was done while the first author was at the Georgia Institute of Technology, Atlanta, USA.
Note: [1] Editor’s note: Early version of this article was included in the Proceedings of OTM 2008 Workshops, LNCS – Lecture Notes in Computer science, Springer, Volume 5333, pp. 150–159, 2008.
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 (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.
