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Improved answer-set programming encodings for abstract argumentation

Published online by Cambridge University Press:  03 September 2015

SARAH A. GAGGL ,
NORBERT MANTHEY ,
ALESSANDRO RONCA ,
JOHANNES P. WALLNER  and
STEFAN WOLTRAN
SARAH A. GAGGL
Affiliation:
Technische Universität Dresden, Germany
NORBERT MANTHEY
Affiliation:
Technische Universität Dresden, Germany
ALESSANDRO RONCA
Affiliation:
La Sapienza, University of Rome
JOHANNES P. WALLNER
Affiliation:
University of Helsinki, Department of Computer Science, HIIT
STEFAN WOLTRAN
Affiliation:
Vienna University of Technology, Austria
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Abstract

The design of efficient solutions for abstract argumentation problems is a crucial step towards advanced argumentation systems. One of the most prominent approaches in the literature is to use Answer-Set Programming (ASP) for this endeavor. In this paper, we present new encodings for three prominent argumentation semantics using the concept of conditional literals in disjunctions as provided by the ASP-system clingo. Our new encodings are not only more succinct than previous versions, but also outperform them on standard benchmarks.

Keywords

Answer-Set ProgrammingAbstract ArgumentationImplementationASPARTIX
Type
Regular Papers
Information
Theory and Practice of Logic Programming , Volume 15 , Special Issue 4-5: 31st International Conference on Logic Programming , July 2015 , pp. 434 - 448
Copyright
Copyright © Cambridge University Press 2015 

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References

Baroni, P., Caminada, M. W. A. and Giacomin, M. 2011. An Introduction to Argumentation Semantics. The Knowledge Engineering Review 26, 4, 365–410.Google Scholar
Besnard, P. and Hunter, A. 2008. Elements of Argumentation. MIT Press.Google Scholar
Bistarelli, S. and Santini, F. 2011. ConArg: A constraint-based computational framework for argumentation systems. In Proceedings of the 23rd IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2011), Khoshgoftaar, T. M. and Zhu, X. H., Eds. IEEE Computer Society Press, 605–612.Google Scholar
Brewka, G., Eiter, T. and Truszczyński, M. 2011. Answer set programming at a glance. Communications of the ACM 54, 12, 92–103.Google Scholar
Caminada, M. W. A. 2007. Comparing Two Unique Extension Semantics for Formal Argumentation: Ideal and Eager. In Proceedings of the 19th Belgian-Dutch Conference on Artificial Intelligence, BNAIC 2007. 81–87.Google Scholar
Caminada, M. W. A. and Amgoud, L. 2007. On the Evaluation of Argumentation Formalisms. Artificial Intelligence 171, 5–6, 286–310.CrossRefGoogle Scholar
Caminada, M. W. A., Carnielli, W. A. and Dunne, P. E. 2012. Semi-Stable Semantics. Journal of Logic and Computation 22, 5, 1207–1254.Google Scholar
Cerutti, F., Giacomin, M. and Vallati, M. 2014. ArgSemSAT: solving argumentation problems using SAT. In Proceedings of the 5th International Conference on Computational Models of Argument (COMMA 2014), Parsons, S., Oren, N., Reed, C., and Cerutti, F., Eds. FAIA, vol. 266. IOS Press, 455–456.Google Scholar
Cerutti, F., Oren, N., Strass, H., Thimm, M. and Vallati, M. 2014. A benchmark framework for a computational argumentation competition. In Proceedings of the 5th International Conference on Computational Models of Argument (COMMA 2014), Parsons, S., Oren, N., Reed, C., and Cerutti, F., Eds. FAIA, vol. 266. IOS Press, 459–460.Google Scholar
Charwat, G., Dvorák, W., Gaggl, S. A., Wallner, J. P. and Woltran, S. 2015. Methods for solving reasoning problems in abstract argumentation - A survey. Artificial Intelligence 220, 28–63.Google Scholar
Dimopoulos, Y. and Torres, A. 1996. Graph Theoretical Structures in Logic Programs and Default Theories. Theoretical Computer Science 170, 1–2, 209–244.Google Scholar
Dung, P. M. 1995. On the Acceptability of Arguments and its Fundamental Role in Nonmonotonic Reasoning, Logic Programming and n-Person Games. Artificial Intelligence 77, 2, 321–358.Google Scholar
Dung, P. M., Mancarella, P. and Toni, F. 2007. Computing Ideal Sceptical Argumentation. Artificial Intelligence 171, 10–15, 642–674.Google Scholar
Dunne, P. E. and Bench-Capon, T. J. M. 2002. Coherence in finite argument systems. Artificial Intelligence 141, 1/2, 187–203.CrossRefGoogle Scholar
Dunne, P. E. and Caminada, M. W. A. 2008. Computational Complexity of Semi-Stable Semantics in Abstract Argumentation Frameworks. In Proceedings of the 11th European Conference on Logics in Artificial Intelligence (JELIA 2008), Hölldobler, S., Lutz, C., and Wansing, H., Eds. LNCS, vol. 5293. Springer, 153–165.Google Scholar
Dunne, P. E., Dvořák, W., Linsbichler, T. and Woltran, S. 2014. Characteristics of multiple viewpoints in abstract argumentation. In Proceedings of the 14th International Conference on Principles of Knowledge Representation and Reasoning (KR 2014), Baral, C., De Giacomo, G., and Eiter, T., Eds. AAAI Press, 72–81.Google Scholar
Dunne, P. E. and Wooldridge, M. 2009. Complexity of Abstract Argumentation. In Argumentation in Artificial Intelligence, Simari, G. and Rahwan, I., Eds. Springer US, 85–104.CrossRefGoogle Scholar
Dvořák, W. and Woltran, S. 2010. Complexity of semi-stable and stage semantics in argumentation frameworks. Information Processing Letters 110, 11, 425–430.CrossRefGoogle Scholar
Dvořák, W., Gaggl, S. A., Wallner, J. P. and Woltran, S. 2013. Making use of advances in answer-set programming for abstract argumentation systems. In Proceedings of the 19th International Conference on Applications of Declarative Programming and Knowledge Management (INAP 2011), Revised Selected Papers, Tompits, H., Abreu, S., Oetsch, J., J. Pührer, Seipel, D., Umeda, M., and Wolf, A., Eds. LNAI, vol. 7773. Springer, 114–133.Google Scholar
Dvořák, W., Järvisalo, M., Wallner, J. P. and Woltran, S. 2014. Complexity-sensitive decision procedures for abstract argumentation. Artificial Intelligence 206, 53–78.Google Scholar
Egly, U., Gaggl, S. A. and Woltran, S. 2010. Answer-set programming encodings for argumentation frameworks. Argument & Computation 1, 2, 147–177.Google Scholar
Gebser, M., Kaminski, R., Kaufmann, B., Lindauer, M., Ostrowski, M., Romero, J., Schaub, T. and Thiele, S. 2015. Potassco User Guide, Second edition ed.Google Scholar
Gebser, M., Kaminski, R. and Schaub, T. 2011. Complex optimization in answer set programming. Theory and Practice of Logic Programming 11, 4–5, 821–839.Google Scholar
Gelfond, M. and Lifschitz, V. 1991. Classical negation in logic programs and disjunctive databases. New Generation Computing 9, 3/4, 365–386.CrossRefGoogle Scholar
Lifschitz, V. and Turner, H. 1994. Splitting a logic program. In Proceedings of the 11th International Conference on Logic Programming (ICLP 1994), Hentenryck, P. V., Ed. MIT Press, 23–37.Google Scholar
Rahwan, I. and Simari, G. R., Eds. 2009. Argumentation in Artificial Intelligence. Springer.Google Scholar
Syrjänen, T. 2009. Logic programs and cardinality constraints: Theory and practice. Ph.D. thesis, Aalto University.Google Scholar
Toni, F. and Sergot, M. 2011. Argumentation and answer set programming. In Logic Programming, Knowledge Representation, and Nonmonotonic Reasoning: Essays in Honor of Michael Gelfond, Balduccini, M. and Son, T. C., Eds. LNCS, vol. 6565. Springer, 164–180.Google Scholar
Vallati, M., Cerutti, F. and Giacomin, M. 2014. Argumentation frameworks features: an initial study. In Proceedings of the 21st European Conference on Artificial Intelligence (ECAI 2014), Schaub, T., Friedrich, G., and O'Sullivan, B., Eds. FAIA, vol. 263. IOS Press, 1117–1118.Google Scholar
Verheij, B. 1996. Two Approaches to Dialectical Argumentation: Admissible Sets and Argumentation Stages. In Proceedings of the Eighth Dutch Conference on Artificial Intelligence (NAIC'96), Meyer, J.-J. C. and van der Gaag, L. , Eds. 357–368.Google Scholar
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