Skip to main content Accessibility help
×
Home

Predictive models and abstract argumentation: the case of high-complexity semantics

  • Mauro Vallati (a1), Federico Cerutti (a2) and Massimiliano Giacomin (a3)

Abstract

In this paper, we describe how predictive models can be positively exploited in abstract argumentation. In particular, we present two main sets of results. On one side, we show that predictive models are effective for performing algorithm selection in order to determine which approach is better to enumerate the preferred extensions of a given argumentation framework. On the other side, we show that predictive models predict significant aspects of the solution to the preferred extensions enumeration problem. By exploiting an extensive set of argumentation framework features—that is, values that summarize a potentially important property of a framework—the proposed approach is able to provide an accurate prediction about which algorithm would be faster on a given problem instance, as well as of the structure of the solution, where the complete knowledge of such structure would require a computationally hard problem to be solved. Improving the ability of existing argumentation-based systems to support human sense-making and decision processes is just one of the possible exploitations of such knowledge obtained in an inexpensive way.

Copyright

References

Hide All
Audemard, G. & Simon, L. 2014. Lazy clause exchange policy for parallel sat solvers. In Proceedings of the 17th International Conference on Theory and Applications of Satisfiability Testing (SAT), 197–205. Springer.
Barabasi, A. & Albert, R. 1999. Emergence of scaling in random networks. Science 286(5439), 509512.
Baroni, P., Caminada, M. & Giacomin, M. 2011. An introduction to argumentation semantics. Knowledge Engineering Review 26(4), 365410.
Baroni, P., Cerutti, F., Dunne, P. & Giacomin, M. 2013. Automata for infinite argumentation structures. Artificial Intelligence 203, 104150.
Baroni, P., Dunne, P. E. & Giacomin, M. 2010. On extension counting problems in argumentation frameworks. In Proceedings of the 3rd International Conference on Computational Models of Argument (COMMA), 216, 63–74. IOS Press.
Baroni, P. & Giacomin, M. 2007. On principle-based evaluation of extension-based argumentation semantics. Artificial Intelligence (Special issue on Argumentation in A.I.) 171(10/15), 675700.
Baroni, P., Giacomin, M. & Guida, G. 2005. SCC-recursiveness: a general schema for argumentation semantics. Artificial Intelligence 168(1–2), 165210.
Baumann, R. & Strass, H. 2014. On the maximal and average numbers of stable extensions. In Proceedings of the 2nd International Workshop on Theory and Applications of Formal Argumentation (TAFA), 111–126. Springer.
Besnard, P. & Hunter, A. 2014. Constructing argument graphs with deductive arguments: a tutorial. Argument & Computation 5(1), 530.
Bistarelli, S., Rossi, F. & Santini, F. 2015a. A comparative test on the enumeration of extensions in abstract argumentation. Fundamenta Informaticae 140(3–4), 263278.
Bistarelli, S., Rossi, F. & Santini, F. 2015b. Testing credulous and sceptical acceptance in smallworld networks. In Proceedings of the 22nd RCRA International Workshop on Experimental Evaluation of Algorithms for Solving Problems with Combinatorial Explosion (RCRA), 39–46.
Brewer, E. A. 1994. Portable High-Performance Supercomputing: High-Level Platform-Dependent OptiMization. PhD thesis, MIT.
Cabrio, E. & Villata, S. 2014. Node: A benchmark of natural language arguments. In Proceedings of the Fifth International Conference on Computational Models of Argument (COMMA), 449–450. IOS Press.
Cerutti, F., Dunne, P., Giacomin, M. & Vallati, M. 2013. Computing preferred extensions in abstract argumentation: a SAT-based approach. In Proceedings of the 2nd International Workshop on Theory and Applications of Formal Argumentation (TAFA), 176–193. Springer.
Cerutti, F., Giacomin, M. & Vallati, M. 2014a. Algorithm selection for preferred extensions enumeration. In Proceedings of the 5th International Conference on Computational Models of Argument (COMMA), 221–232. IOS Press.
Cerutti, F., Giacomin, M. & Vallati, M. 2014b. Generating challenging benchmark AFs. In Proceedings of the 5th International Conference on Computational Models of Argument (COMMA), 457–458. IOS Press.
Cerutti, F., Giacomin, M., Vallati, M. & Zanella, M. 2014c. A SCC recursive meta-algorithm for computing preferred labellings in abstract argumentation. In Proceedings of the 14 th International Conference on Principles of Knowledge Representation and Reasoning (KR). AAAI Press.
Cerutti, F., Vallati, M. & Giacomin, M. 2017. An efficient Java-based solver for abstract argumentation frameworks: jArgSemSAT. International Journal on Artificial Intelligence Tools 26(2), 1750002.
Cerutti, F., Vallati, M. & Giacomin, M. 2018. On the impact of configuration on abstract argumentation automated reasoning. International Journal of Approximate Reasoning 92, 120138.
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), 321357.
Dunne, P. & Wooldridge, M. 2009. Complexity of abstract argumentation. In Argumentation in Artificial Intelligence, 85–104. Springer.
Dunne, P. E. 2007. Computational properties of argument systems satisfying graph-theoretic constraints. Artificial Intelligence 171(10–15), 701729.
Dunne, P. E., Dvořák, W., Linsbichler, T. & Woltran, S. 2015. Characteristics of multiple viewpoints in abstract argumentation. Artificial Intelligence 228, 153178.
Dvořák, W., Gaggl, S., Wallner, J. & Woltran, S. 2011. 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), 114–133. Springer.
Dvorák, W., Järvisalo, M., Wallner, J. P. & Woltran, S. 2015. Cegartix v0. 4: A SAT-based counterexample guided argumentation reasoning tool. In System Descriptions of the First International Competition on Computational Models of Argumentation (ICCMA15), 12.
Dvořák, W., Pichler, R. & Woltran, S 2012. Towards fixed-parameter tractable algorithms for abstract argumentation. Artificial Intelligence 186, 137.
Erdös, P. & Rényi, A. 1959. On random graphs. I. Publicationes Mathematicae Debrecen 6, 290297.
Fawcett, C., Vallati, M., Hutter, F., Hoffmann, J., Hoos, H. & Leyton-Brown, K. 2014. Improved features for runtime prediction of domain-independent planners. In Proceedings of the 24 th International Conference on Automated Planning and Scheduling (ICAPS), 355–359. AAAI Press.
Gebser, M., Kaminski, R., Kaufmann, B., Schaub, T., Schneider, M. T. & Ziller, S. 2011. A portfolio solver for answer set programming: preliminary report. In Proceedings of the 11th International Conference Logic Programming and Nonmonotonic Reasoning (LPNMR), 352–357. Springer.
Gebser, M., Kaufmann, B., Neumann, A. & Schaub, T. 2007. clasp: A conict-driven answer set solver. In Proceedings of the 9th International Conference Logic Programming and Nonmonotonic Reasoning (LPNMR), 260–265. Springer.
Gomes, C. P., Sabharwal, A. & Selman, B. 2009. Model counting. In Handbook of Satisfiability, Biere, A., Heule, M., van Maaren, H. & Walsh, T. (eds). 633654. IOS Press.
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P. & Witten, I. 2009. The WEKA data mining software: an update. SIGKDD Explorations 11(1), 1018.
Hall, M. A. 1998. Correlation-Based Feature Subset Selection for Machine Learning. PhD thesis, University of Waikato, Department of Computer Science, Hamilton, New Zealand.
Holmes, G., Hall, M. & Prank, E. 1999. Generating Rule Sets from Model Trees. Springer.
Hoos, H., Lindauer, M. T. & Schaub, T. 2014. claspfolio 2: Advances in algorithm selection for answer set programming. Theory and Practice of Logic Programming 14(4–5), 569585.
Hutter, F., Xu, L., Hoos, H. & Leyton-Brown, K. 2014. Algorithm runtime prediction: methods & evaluation. Artificial Intelligence 206, 79111.
Kohavi, R. 1995. The power of decision tables. In 8th European Conference on Machine Learning, 174–189. Springer.
Kröll, M., Pichler, R. & Woltran, S 2017. On the complexity of enumerating the extensions of abstract argumentation frameworks. In Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI, 1145–1152.
Leyton-Brown, K., Nudelman, E. & Shoham, Y. 2009. Empirical hardness models: methodology and a case study on combinatorial auctions. Journal of the ACM 56(4), 152.
Luo, J. & Magee, C. L. 2011. Detecting evolving patterns of self-organizing networks by flow hierarchy measurement. Complexity 16(6), 5361.
Maratea, M., Pulina, L. & Ricca, F. 2014. A multi-engine approach to answer-set programming. Theory and Practice of Logic Programming 14(6), 841868.
Marquardt, D. W. & Snee, D. 1975. Ridge regression in practice. The American Statistician 29(1), 320.
Matos, P., Planes, J., Letombe, F. & Marques-Silva, J. 2008. A MAX-SAT algorithm portfolio. In Proceedings of the 18th European Conference on Artificial Intelligence (ECAI), 911–912. IOS Press.
Miller, G. A. 1956. The magical number seven, plus or minus two: some limits on our capacity for processing information. Psychological Review 63, 81.
Nofal, S., Atkinson, K. & Dunne, P. 2014. Algorithms for decision problems in argument systems under preferred semantics. Artificial Intelligence 207, 2351.
Nudelman, E., Leyton-Brown, K., Devkar, A., Shoham, Y. & Hoos, H. 2004. Understanding random SAT: Beyond the clauses-to-variables ratio. In Proceedings of the 10th International Conference on Principles and Practice of Constraint Programming (CP), 438–452. Springer.
Pulina, L. & Tacchella, A. 2007. A multi-engine solver for quantified Boolean formulas. In Proceedings of the 13th International Conference on Principles and Practice of Constraint Programming (CP), 574–589. Springer.
Sideris, A. & Dimopoulos, Y. 2010. Constraint propagation in propositional planning. In Proceedings of the 20th International Conference on Automated Planning and Scheduling (ICAPS), 153–160. AAAI Press.
Smith-Miles, K., van Hemert, J. & Lim, X. 2010. Understanding TSP difficulty by learning from evolved instances. In Proceedings of the 4th International Conference on Learning and Intelligent Optimization (LION), 266–280. Springer.
Tamani, N., Mosse, P., Croitoru, M., Buche, P., Guillard, V., Guillaume, C. & Gontard, N. 2015. An argumentation system for eco-efficient packaging material selection. Computers and Electronics in Agriculture 113, 174192.
Thimm, M. & Villata, S. 2017. The first international competition on computational models of argumentation: results and analysis. Artificial Intelligence 252, 267294.
Thimm, M., Villata, S., Cerutti, F., Oren, N., Strass, H. & Vallati, M. 2016. Summary report of the first international competition on computational models of argumentation. AI Magazine 37(1), 102.
Toniolo, A., Norman, T. J., Etuk, A., Cerutti, F., Ouyang, R. W., Srivastava, M., Oren, N., Dropps, T., Allen, J. A. & Sullivan, P. 2015. Agent support to reasoning with different types of evidence in intelligence analysis. In Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 781–789. ACM.
Valiant, L. 1979. The complexity of computing the permanent. Theoretical Computer Science 8(2), 189201.
Vallati, M., Cerutti, F. & Giacomin, M. 2014. Argumentation frameworks features: an initial study. In Proceedings of the 21st European Conference on Artificial Intelligence (ECAI), 1117–1118. IOS Press.
Watts, D. J. & Strogatz, S. H. 1998. Collective dynamics of 'small-world’ networks. Nature 393(6684), 440442.
Wyner, A., Bench-Capon, T., Dunne, P. & Cerutti, F. 2015. Senses of argument in instantiated argumentation frameworks. Argument & Computation 6(1), 5072.
Xu, L., Hutter, F., Hoos, H. & Leyton-Brown, K. 2008. SATzilla: portfolio-based algorithm selection for SAT. Journal of Artificial Intelligence Research 32, 565606.

Metrics

Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed