Argumentation is an interdisciplinary research area that incorporates many fields such as artificial intelligence, multi-agent systems, and collaborative learning. In this chapter, we describe argument mining techniques from a structured argument database "RADB", a sort of relational database we designed specially for organizing argument databases, and their usage in arguing agents and intelligent tutoring systems. The RADB repository depends on the Argumentation Interchange Format Ontology (AIF) using "Walton Theory" for argument analysis. It presents a novel approach that summarizes the argument data set into structured form "RADB" in order to (i) facilitate the data interoperability among various agents/humans/tools, (ii) provide the ability to freely navigate the repository by integrating the data mining techniques gathered in a classifier agent; mine the RADB repository and retrieve the most relevant arguments to the users' queries, (iii) illustrate an agent-based learning environment outline, where the mining classifier agent and the RADB are incorporated together within an intelligent tutoring system (ITS). Such incorporation assists in (i) deepening the understanding of negotiation, decision making, and critical thinking, (ii) guiding the analysis process to refine the user's underlying classification, and improving the analysis and the students' intellectual process. Later in the chapter, we describe an effective usage of argument mining for arguing agents, which interact with each other in the Internet environment and argues about issues concerned, casting arguments and counter-arguments each other to reach an agreement. We illustrate how argument mining allows to strengthen arguing agent intelligence, resulting in expanding the main concern in formal argumentation frameworks that is to formalize methods in which the final statuses of arguments are to be decided semantically and/or dialectically. In both usages, we yield new forms of argument-based intelligence, which allows establishing one's own argument by comparing diverse views and opinions and uncovering new leads, differently from simple refutation aiming at cutting down other parties. © 2010 Springer-Verlag Berlin Heidelberg.
Abbas, S., & Sawamura, H. (2010). Argument mining from RADB and its usage in arguing agents and intelligent tutoring system. Studies in Computational Intelligence, 310, 113–147. https://doi.org/10.1007/978-3-642-14435-6_5