Argumentation Based Machine Learning for Inconsistent Knowledge Bases

Abstract

Knowledge integration in distributed data mining has received widespread attention that aims to integrate inconsistent information locating on distributed sites. Traditional integration methods become ineffective since they are unable to generate global knowledge, support advanced integration strategy, or make prediction without individual classifiers. In this paper, we propose an argumentation based reinforcement learning method to handle this problem. To this end, a constructive model to merge possiblistic belief bases built based on the famous general argumentation framework is proposed. An axiomatic model, including a set of rational and intuitive postulates to characterize the merging result is introduced and several logical properties are mentioned and discussed.