TítuloCoordinated inductive learning using argumentation-based communication
Publication TypeJournal Article
Year of Publication2015
AuthorsOntañón S, Plaza E
JournalAutonomous Agents and Multi-Agent Systems
Volume29
Paginación266-304
Date Published03/2015
EditorialSpringer
Palabras claveArgumentation, CBR, Inductive Learning, Learning from Communication, Machine Learning
Resumen

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.

URLhttp://link.springer.com/article/10.1007%2Fs10458-014-9256-2