Abstract
Computational argumentation has been accepted as a social computing mechanism or paradigm in the multi-agent systems community. In this paper, we are further concerned with what agents believe after argumentation, such as how agents should accommodate justified arguments into their knowledge bases after argumentation, what and how agents acquire or learn, based on the results of argumentation. This is an attempt to create a new learning method induced by argumentation that we call Argument-Based Learning (ABL). To this end, we use our logic of multiple-valued argumentation LMA built on top of Extended Annotated Logic Programming EALP, and propose three basic definitions to capture agents’ beliefs that should be rationally acquired after argumentation: knowledge acquisition induced by the undercut of assumptions, knowledge acquisition induced by difference of recognition, and knowledge acquisition induced by rebut. They are derived from two distinctive and advantageous apparatuses of our approach to multi-valued argumentation under : Paraconsistency and multiple-valuedness that EALP and LMA feature. We describe an overall argument example to show the effectiveness and usefulness of the agent learning methods based on argumentation.
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Fukumoto, T., Sawamura, H. (2007). Argumentation-Based Learning. In: Maudet, N., Parsons, S., Rahwan, I. (eds) Argumentation in Multi-Agent Systems. ArgMAS 2006. Lecture Notes in Computer Science(), vol 4766. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75526-5_2
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DOI: https://doi.org/10.1007/978-3-540-75526-5_2
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