Abstract
This paper introduces the use of fuzzy labels in argumentation. The first approach we propose is built as a natural extension of the in, out, undec labeling to real valued labels, coupled with an unsupervised learning algorithm that assigns consistent labels starting from a random initial assignment. The second approach regards argument (fuzzy) labels as degrees of certitude in the argument’s acceptability. This translates into a system of equations that provides among its solutions the labelings that describe complete extensions.
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
Baroni, P., Giacomin, M.: Resolution-based argumentation semantics. In: Proceedings of the 2nd Conference on Computational Models of Argument (COMMA 2008), Tolouse, France, pp. 25–36 (2008)
Baroni, P., Giacomin, M., Guida, G.: SCC-recursiveness: a general schema for argumentation semantics. Artificial Intelligence 168(1-2), 162–210 (2005)
Caminada, M.: Semi-stable semantics. In: Dunne, P.E., Bench-Capon, T.J.M. (eds.) Computational Models of Argument: Proceedings of COMMA 2006, September 11–12. Frontiers in Artificial Intelligence and Applications, vol. 144, pp. 121–130. IOS Press, Liverpool (2006)
Caminada, M.: Comparing two unique extension semantics for formal argumentation: Ideal and eager. In: Proceedings of the 19th Benelux Conference on Artificial Intelligence (BNAIC 2007), pp. 81–87 (2007)
Caminada, M., Gabbay, D.M.: A logical account of formal argumentation. Studia Logica 93(2-3), 109–145 (2009)
Coste-Marquis, S., Devred, C., Marquis, P.: Prudent semantics for argumentation frameworks. In: Proceedings of the 17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2005), Hong-Kong, China, pp. 568–572 (2005)
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)
Dung, P.M., Mancarella, P., Toni, F.: Computing ideal sceptical argumentation. Artificial Intelligence 171(10-15), 642–674 (2007)
Gabbay, D.M.: Fibring argumentation frames. Studia Logica 93(2-3), 231–295 (2009)
Janssen, J., De Cock, M., Vermeir, D.: Fuzzy argumentation frameworks. In: Proceedings of the 12th Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU 2008), pp. 513–520 (2008)
Verheij, B.: Two approaches to dialectical argumentation: admissible sets and argumentation stages. In: Meyer, J.J.C., van der Gaag, L.C. (eds.) Proceedings of the 8th Dutch Conference on Artificial Intelligence (NAIC 1996), Utrecht, Netherlands, pp. 357–368 (1996)
Zhang, Z., Lin, Z.: Enhancing Dung’s Preferred Semantics. In: Link, S., Prade, H. (eds.) FoIKS 2010. LNCS, vol. 5956, pp. 58–75. Springer, Heidelberg (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Gratie, C., Florea, A.M. (2012). Fuzzy Labeling for Argumentation Frameworks. In: McBurney, P., Parsons, S., Rahwan, I. (eds) Argumentation in Multi-Agent Systems. ArgMAS 2011. Lecture Notes in Computer Science(), vol 7543. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33152-7_1
Download citation
DOI: https://doi.org/10.1007/978-3-642-33152-7_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33151-0
Online ISBN: 978-3-642-33152-7
eBook Packages: Computer ScienceComputer Science (R0)