|Título||Coordinated inductive learning using argumentation-based communication|
|Publication Type||Journal Article|
|Year of Publication||2015|
|Authors||Ontañón S, Plaza E|
|Journal||Autonomous Agents and Multi-Agent Systems|
|Palabras clave||Argumentation, CBR, Inductive Learning, Learning from Communication, Machine Learning|
This paper focuses on coordinated inductive learning (CIL), concerning how agents with inductive learning capabilities can coordinate their learnt hypotheses with other agents. Coordination in this context means that the hypothesis learnt by one agent is consistent with the data known to the other agents. In order to address this problem, we present A-MAIL, an argumentation approach for agents to argue about hypotheses learnt by induction. A-MAIL integrates, in a single framework, the capabilities of learning from experience, communication, hypothesis revision and argumentation. Therefore, the A-MAIL approach is one step further in achieving autonomous agents with learning capabilities which can use, communicate and reason about the knowledge they learn from examples.
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