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.
Keywords
- Argumentation Framework
- Prediction Market
- Argumentation Process
- Individual Accuracy
- Case Base Reasoning System
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Ontañón, S., Plaza, E. (2008). Learning, Information Exchange, and Joint-Deliberation through Argumentation in Multi-agent Systems. In: Meersman, R., Tari, Z., Herrero, P. (eds) On the Move to Meaningful Internet Systems: OTM 2008 Workshops. OTM 2008. Lecture Notes in Computer Science, vol 5333. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88875-8_34
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DOI: https://doi.org/10.1007/978-3-540-88875-8_34
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