Exploiting Ontological Reasoning in Argumentation Based Multi-agent Collaborative Classification

Abstract

Argumentation-based multi-agent collaborative classification is a promising paradigm for reaching agreements in distributed environments. In this paper, we advance the research by introducing a new domain ontology enriched inductive learning approach for collaborative classification, in which agents are able to constructing arguments taking into account their own domain knowledge. This paper focuses on classification rules inductive learning, and presents Arguing SATE-Prism, a domain ontology enriched approach for multi-agent collaborative classification based on argumentation. Domain ontology, in this context, is exploited for driving a paradigm shift from traditional data-centered hidden pattern mining to domain-driven actionable knowledge discovery. Preliminary experimental results show that higher classification accuracy can be achieved by exploiting ontological reasoning in argumentation based multi-agent collaborative classification. Our experiments also demonstrate that the proposed approach out-performs comparable classification paradigms in presence of instances with missing values, harnessing the advantages offered by ontological reasoning.