A mathematical treatment of defeasible reasoning and its implementation
Artificial Intelligence
Inferring from Inconsistency in Preference-Based Argumentation Frameworks
Journal of Automated Reasoning
Learning Logical Definitions from Relations
Machine Learning
Argument based machine learning in a medical domain
Proceedings of the 2006 conference on Computational Models of Argument: Proceedings of COMMA 2006
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Towards a Logical Model of Induction from Examples and Communication
Proceedings of the 2010 conference on Artificial Intelligence Research and Development: Proceedings of the 13th International Conference of the Catalan Association for Artificial Intelligence
A defeasible reasoning model of inductive concept learning from examples and communication
Artificial Intelligence
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Argumentation is a promising approach used by autonomous agents for reasoning about inconsistent knowledge, based on the construction and the comparison of arguments. In this paper, we apply this approach to the classification problem, whose purpose is to construct from a set of training examples a model (or hypothesis) that assigns a class to any new example. We propose a general formal argumentation-based model that constructs arguments for/against each possible classification of an example, evaluates them, and determines among the conflicting arguments the acceptable ones. Finally, a "valid" classification of the example is suggested. Thus, not only the class of the example is given, but also the reasons behind that classification are provided to the user as well in a form that is easy to grasp. We show that such an argumentation-based approach for classification offers other advantages, like for instance classifying examples even when the set of training examples is inconsistent, and considering more general preference relations between hypotheses. Moreover, we show that in the particular case of concept learning, the results of version space theory are retrieved in an elegant way in our argumentation framework.