Towards a Connectionist Argumentation Framework
نویسندگان
چکیده
While neural networks have been successfully used in a number of machine learning applications, logical languages have been the standard for the representation of legal and argumentative reasoning [6]. In this paper, we present a new hybrid model of computation that allows for the deduction and learning of argumentative reasoning. We do so by using Neural-Symbolic Learning Systems to translate argumentation networks into standard neural networks. The approach enables cummulative argumentation through learning, as the strength of the arguments change over time.
منابع مشابه
A neural cognitive model of argumentation with application to legal inference and decision making
Formal models of argumentation have been investigated in several areas, from multi-agent systems and artificial intelligence (AI) to decision making, philosophy and law. In artificial intelligence, logic-based models have been the standard for the representation of argumentative reasoning. More recently, the standard logicbased models have been shown equivalent to standard connectionist models....
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