Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Abbas, S., Sawamura, H.: Argument mining using highly structured argument repertoire. In: The First International Conference on Educational Data Mining (EDM), Montreal, Québec, Canada, pp. 202–210 (2008)
Abbas, S., Sawamura, H.: A first step towards argument mining and its use in arguing agents and its. In: Lovrek, I., Howlett, R.J., Jain, L.C. (eds.) KES 2008, Part I. LNCS (LNAI), vol. 5177, pp. 149–157. Springer, Heidelberg (2008)
Abbas, S., Sawamura, H.: Towards argument mining using relational argument database. In: The Second International Workshop on Juris-informatics (JURISIN), Asahikawa Convention Bureau, Hokkaido, Japan, pp. 22–31 (2008)
Agrawal, R., Srikant, R.: Fast algorithms for mining rules. In: The 20th VLDB Conference Santiago, Chile (1994)
Chesnevar, C., McGinnis, J., Modgil, S., Rahawan, I.: Towards an argument interchange format. In: The Knowledge Engineering Review, Cambridge University Press, Cambridge
Chesñevar, C.I., Maguitman, G., Loui, R.P.: Logical models of argument. ACM Computing Surveys 32, 337–383 (2000)
Chi, Y., Muntz, R.: Frequent subtree mining-an overview. Fundamenta Informaticae, 1001–1038 (2001)
Conrad, J., Schilder, F.: Opinion mining in legal blogs. In: ICAIL 2007, Palo Alto, California, USA, June 4-8, 2007, pp. 231–236 (2007)
Katzav, J., Reed, C., Rowe, G.: Argument research corpus. In: Huget, M.-P. (ed.) Communication in Multiagent Systems. LNCS, vol. 2650, pp. 269–283. Springer, Heidelberg (2003)
Macagno, F., Walton, D.: Argumentative reasoning patterns. In: Proceeding of 6th CMNA (Computational Models of Natural Argument) Workshop, ECAI (European Conference on Aetificial Intelligence), Rivadel Garda, Italy, Trento, Italy, University of Trento, pp. 48–51 (2006)
McBurney, P., Parsons, S.: Dialogue game protocols. In: Practical Applications in Language Corpora (2003)
Godden, M., Walton, D.: Argument from expert opinion as lgal evidence: Critical questions and admissibility criteria of expert testimony in the american legal system. In: R. Juris, vol. 19, pp. 261–286 (2006)
Moens, M., Boiy, E., Palau, R., Reed, C.: Automatic detection of arguments in legal texts. In: ICAIL 2007, Palo Alto, California, USA, June 4-8, 2007, pp. 225–230 (2007)
Zaki, M.: Efficiently mining frequent trees in a forest: Algorithms and applications. IEEE transactions on knowledge and data engineering 17, 1021–1035 (2005)
Nijssen, S., Kok, J.N.: A quickstart in frequent structure mining can make a difference. In: Tenth ACM SIGKDD international conference on Knowledge discovery and data mining, USA, pp. 647–652 (2004)
Prakken, H., Vreeswijk, G.: Logical systems for defeasible argumentation. In: Gabbay, D., Guenther, F. (eds.) Handbook of Philosophical Logic, pp. 219–318. Kluwer, Dordrecht (2002)
Rahawan, I., Sakeer, P.: Representing and querying arguments on semantic web. In: Dunne, P.E., Bench-Capon, T.J.M. (eds.) Computational Models of Argument, IOS Press, Amsterdam (2006)
Rahawan, I., Zablith, F., Reed, C.: The foundation for a world wide argument web. In: Artificial Intelligence Conference (AAAI), April 04 (2007) (published in the Artificial Intelligence Journal)
Reed, C., Norman, T.J. (eds.): Argumentation Machines. Kluwer Academinc Publishers, Dordrecht (2004)
Reed, C., Rowe, G.: Araucaria: Software for argument analysis, diagramming and representation. International Journal on Artificial Intelligence Tools 13, 983 (2004)
Reed, C., Rowe, G.: Translating toulmin diagrams: Theory neutrality in argument representation. Argumentation Journal 19, 267–286 (2005)
Rowe, G., Reed, C., Katzav, J.: Araucaria: Making up argument. In: European Conference on Computing and Philosophy (2003)
Takahashi, T., Sawamura, H.: A logic of multiple-valued argumentation. In: Proceedings of the third international joint conference on Autonomous Agents and Multi Agent Systems (AAMAS 2004), pp. 800–807. ACM, New York (2004)
Takahashi, Y., Sawamura, H., Zhang, J.: Transforming natural arguments in araucaria to formal arguments in lma. In: Proc. of 2006 IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT 2006), pp. 668–678. IEEE Computer Society Press, Los Alamitos (2006)
Walton, D.: Argument from appearance: a new argumentation scheme. Ligique et Analyse 195, 319–340 (2006)
Walton, D., Hansen, H.: Fundamentals of critical argumentation. Cambridge University Press, Cambridge, New York, Melbourne (2006)
Walton, D., Rowe, G., Macagno, F., Reed, C.: Araucaria as a tool for diagramming arguments in teaching and studying philosophy. Teaching Philosophy 29, 111–124 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Abbas, S., Sawamura, H. (2010). Argument Mining from RADB and Its Usage in Arguing Agents and Intelligent Tutoring System. In: Srinivasan, D., Jain, L.C. (eds) Innovations in Multi-Agent Systems and Applications - 1. Studies in Computational Intelligence, vol 310. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14435-6_5
Download citation
DOI: https://doi.org/10.1007/978-3-642-14435-6_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-14434-9
Online ISBN: 978-3-642-14435-6
eBook Packages: EngineeringEngineering (R0)