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 aMAC system can undertake thanks to the argumentation processes.
CITATION STYLE
Ontañón, S., & Plaza, E. (2008). Learning, information exchange, and joint-deliberation through argumentation in multi-agent systems. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5333, pp. 150–159). Springer Verlag. https://doi.org/10.1007/978-3-540-88875-8_34
Mendeley helps you to discover research relevant for your work.