Abstract
Formal models of argumentation have been studied in several areas, notably in logic, philosophy, decision making, artificial intelligence, and law [25, 31, 39, 48, 83, 111, 153, 210, 212, 267]. In artificial intelligence, models of argumentation have been one of the approaches used in the representation of commonsense, nonmonotonic reasoning. They have been particularly successful in modelling chains of defeasible arguments so as to reach a conclusion [194, 209]. Although symbolic logic-based models have been the standard for the representation of argumentative reasoning [31, 108], such models are intrinsically related to artificial neural networks, as we shall show in this chapter.
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© 2009 Springer-Verlag Berlin Heidelberg
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(2009). Argumentation Frameworks as Neural Networks. In: Neural-Symbolic Cognitive Reasoning. Cognitive Technologies. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73246-4_11
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DOI: https://doi.org/10.1007/978-3-540-73246-4_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73245-7
Online ISBN: 978-3-540-73246-4
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