Arguing and Explaining Classifications

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 classication 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 classication of an example, evaluates them, and determines among the con icting arguments the acceptable ones. Finally, a \valid" classication of the example is suggested. Thus, not only the class of the example is given, but also the reasons behind that classication are provided to the user as well in a form that is easy to grasp. We show that such an argumentation-based approach for classication oers other advantages, like for instance classifying examples even when the set of training examples is inconsistent, and...

• Type: Conference
• Year: 2007
• Where: ARGMAS
• Authors: Leila Amgoud, Mathieu Serrurier
• Keywords: ARGMAS 2007, Classi Cation, Classi Cation Problem, Intelligent Agents, Possible Classi Cation