Argumentation is an interdisciplinary research area that incorporates many fields such as artificial intelligence, multi-agent systems, and collaborative learning. Although different argumentation tools have been developed, a structured data representation format has been missing. Recent researches have focused on applying mining techniques to find meaningful knowledge from these unstructured textual data. This paper reports work in progress on building Relational Argument DataBase(RADB) for argument mining and its use in arguing agents and ITS. The RADB depends on the Argumentation Interchange Format Ontology (AIF) using "Walton Theory" for argument analysis. Our aim is to present a preliminary attempt to support argument construction for agents and/or humans from structured argument database together with different mining techniques. We also discuss the usage of relational argument database in agent-based intelligent tutoring system(ITS) framework.
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