Abstract
This paper presents an approach that integrates notions and techniques from two distinct fields of study —namely inductive learning and argumentation in multiagent systems (MAS). We will first discuss inductive learning and the role argumentation plays in multiagent inductive learning. Then we focus on how inductive learning can be used to realize argumentation in MAS based on empirical grounds. We present a MAS framework for empirical argumentation, A-MAIL , and then we show how this is applied to a particular task where two agents argue in order to reach agreement on a particular topic. Finally, an experimental evaluation of the approach is presented evaluating the quality of the agreements achieved by the empirical argumentation process.
Categories and Subject Descriptors
I.2.11 [Artificial Intelligence]: Distributed Artificial Intelligence — Multiagent systems, Intelligent Agents.
I.2.6 [Artificial Intelligence]: Learning.
General Terms: Algorithms, Experimentation, Theory.
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References
Amgoud, L., Serrurier, M.: Arguing and explaining classifications. In: Rahwan, I., Parsons, S., Reed, C. (eds.) ArgMAS 2007. LNCS (LNAI), vol. 4946, pp. 164–177. Springer, Heidelberg (2008)
Chesñevar, C.I., Simari, G.R., Godo, L.: Computing dialectical trees efficiently in possibilistic defeasible logic programming. In: Baral, C., Greco, G., Leone, N., Terracina, G. (eds.) LPNMR 2005. LNCS (LNAI), vol. 3662, pp. 158–171. Springer, Heidelberg (2005)
Clark, P., Niblett, T.: The CN2 induction algorithm. Machine Learning, 261–283 (1989)
Davies, W., Edwards, P.: The communication of inductive inferences. In: Weiss, G. (ed.) ECAI 1996 Workshops. LNCS, vol. 1221, pp. 223–241. Springer, Heidelberg (1997)
Dietterich, T.G.: Ensemble methods in machine learning. In: Kittler, J., Roli, F. (eds.) MCS 2000. LNCS, vol. 1857, pp. 1–15. Springer, Heidelberg (2000)
Dung, P.M.: On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming and n-person games. Artificial Intelligence 77(2), 321–357 (1995)
Gómez, S.A., Chesñevar, C.I.: Integrating defeasible argumentation and machine learning techniques. In: CoRR, cs.AI/0402057 (2004)
Mitchell, T.: Machine Learning. McGraw-Hill, New York (1997)
Mozina, M., Zabkar, J., Bratko, I.: Argument based machine learning. Artificial Intelligence 171(10-15), 922–937 (2007)
Ontañón, S., Plaza, E.: Case-based learning from proactive communication. In: IJCAI, pp. 999–1004 (2007)
Ontañón, S., Plaza, E.: Learning and joint deliberation through argumentation in multiagent systems. In: AAMAS 2007, pp. 971–978 (2007)
Ontañón, S., Plaza, E.: Multiagent inductive learning: an argumentation-based approach. In: ICML, pp. 839–846 (2010)
Quinlan, J.R.: Learning logical definitions from relations. Machine Learning 5, 239–266 (1990)
Rahwan, I., Simari, G.R.: Argumentation in Artificial Intelligence. Springer, Heidelberg (2009)
Wardeh, M., Bench-Capon, T.J.M., Coenen, F.: PADUA: a protocol for argumentation dialogue using association rules. Artificial Intelligence in Law 17(3), 183–215 (2009)
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Ontañón, S., Plaza, E. (2011). Empirical Argumentation: Integrating Induction and Argumentation in MAS. In: McBurney, P., Rahwan, I., Parsons, S. (eds) Argumentation in Multi-Agent Systems. ArgMAS 2010. Lecture Notes in Computer Science(), vol 6614. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21940-5_4
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DOI: https://doi.org/10.1007/978-3-642-21940-5_4
Publisher Name: Springer, Berlin, Heidelberg
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