Multilevel Aggregation of Arguments in a Model Driven Approach to Assess an Argumentation Relevance

Abstract

Figuring out which hypothesis best explain an observed ongoing situation can be a critical issue. This paper introduces a generic model based approach to support users during this task. It then focuses on an hypothesis relevance scoring function that helps users to efficently build a convincing argumentation towards hypothesis. This function uses a multi-level extension of Yager's aggregation algorithm, exploiting both the strength of the components of an argumentation, and the confidence the user puts in them. The presented work was illustrated on a maritime surveillance application. © Springer International Publishing Switzerland 2014.