Possibilistic-based argumentation: An answer set programming approach

Proceedings of the Mexican International Conference on Computer Science

Volumn , Issue , 2008, Pages 249-260

  Nieves, Juan Carlos ^a   Cortés, Ulises ^a   Osorio, Mauricio ^b  

^a UNIVERSITAT POLITÈCNICA DE CATALUNYA   (Spain)

^b UNIVERSIDAD DE LAS AMÉRICAS PUEBLA   (Mexico)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER SCIENCE; COMPUTERS; KNOWLEDGE BASED SYSTEMS;

ANSWER SET PROGRAMMINGS; ARGUMENTATION THEORIES; AUTOMATED INFORMATIONS; INCOMPLETE INFORMATIONS; KNOWLEDGE BASE; UNCERTAIN REASONINGS;

LINGUISTICS;

EID: 56649099356     PISSN: 15504069     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ENC.2008.37     Document Type: Conference Paper

References (48)

1. T. Alsinet, C. I. Chesñevar, L. Godo, S. Sadri, and G. R. Simari. Formalizing argumentative reasoning in a possibilistic logic programming setting with fuzzy unification. International Journal of Approximate Reasoning, page in press, 2008.
2. T. Alsinet, C.I. Chesñevar, L. Godo, and G. R. Simari. A logic programming framework for possibilistic argumentation: Formalization and logical properties. Fuzzy Sets and Systems, 159(10):1208-1228, 2008.
3. T. Alsinet and L. Godo. A Ccomplete Calculus for Possibilistic Logic Programming with Fuzzy Propositional Variable. In Proceedings of the Sixteen Conference on Uncertainty in Artificial Intelligence, 1-10, 2000. ACM Press.
4. T. Alsinet and L. Godo. Towards an automated deduction system for first-order possibilistic logic programming with fuzzy constants. Int. J. Intell. Syst., 17(9):887-924, 2002.
5. L. Amgoud and C. Cayrol. Inferring from inconsistency in preference-based argumentation frameworks. J. Autom. Reasoning, 29(2):125-169, 2002.
6. L. Amgoud and H. Prade. Using arguments for making decisions: A possibilistic logic approach. In Proceedings of the 20th Annual Conference on Uncertainty in Artificial Intelligence (UAI-04), pages 10-17, Arlington, Virginia, 2004. AUAI Press.
7. ASPIC: Project. Deliverable D2.1: Theoretical framework for argumentation. Argumentation Service Plarform with Integrated Components, 2004.
8. J. F. Baldwin. Evidential support logic programming. Fuzzy Sets and Systems, 24(1):1-26, Octuber 1987.
9. C. Baral. Knowledge Representation, Reasoning and Declarative Problem Solving. Cambridge University Press, Cambridge, 2003.
10. P. Baroni and M. Giacomin. On principle-based evaluation of extension-based argumentation semantics. Artificial Intelligence., 171(10-15):675-700, 2007.
11. B. Bonet and H. Geffner. Arguing for decisions: A qualitative model of decision making. In Proceedings of the 12th Annual Conference on Uncertainty in Artificial Intelligence (UAI-96), pages 98-105, San Francisco, CA, 1996. Morgan Kaufmann Publishers.
12. G. Brewka. Answer sets: From constraint programming towards qualitative optimization. In V. Lifschitz and I. Niemelä, editors, Logic Programming and Nonmonotonic Reasoning, 7th International Conference, LPNMR 2004, Fort Lauderdale, FL, USA, January 6-8, 2004, Proceedings, volume 2923 of Lecture Notes in Computer Science, pages 34-46. Springer, 2004.
13. th Workshop on Computational Models of Natural Argument, ECAI 2004, pages ?-?, Valencia, Spain, 2004.
14. S. DLV. Vienna University of Technology. http://www.dbai.tuwien.ac.at/ proj/dlv/, 1996.
15. D. Dubois, J. Lang, and H. Prad. Towards possibilistic logic programming. In K. Furukawa, editor, ICLP, pages 581-595. The MIT Press, 1991.
16. D. Dubois, J. Lang, and H. Prade. Possibilistic logic. In D. Gabbay, C. J. Hogger, and J. A. Robinson, editors, Handbook of Logic in Artificial Intelligence and Logic Programming, Volume 3: Nonmonotonic Reasoning and Uncertain Reasoning, pages 439-513. Oxford University Press, Oxford, 1994.
17. D. Dubois and H. Prade. Possibilistic logic: a retrospective and prospective view. Fuzzy Sets and Systems, 144(1):3-23, 2004.
18. P. M. Dung. On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming and n-person games. Artificial Intelligence, 77(2):321-358, 1995.
19. M. Fitting. Bilattices and the semantics of logic programming. Journal of Logic Programming, 11(1&2):91-116, 1991.
20. J. Fox and S. Modgil. From arguments to decisions: Extending the Toulmin view. In D. Hitchcock and B. Verheij, editors, Arguing on the Toulmin model: New essays on argument analysis and evaluation, pages 273-287. Springer Netherlands, 2006.
21. J. Fox and S. Parsons. Applications of Uncertainty Formalisms, chapter Arguing about beliefs and actions, pages 266-302. Springer-Verlag, Berlin, 1998.
22. M. Gelfond and V. Lifschitz. The Stable Model Semantics for Logic Programming. In R. Kowalski and K. Bowen, editors, 5th Conference on Logic Programming, pages 1070-1080. MIT Press, 1988.
23. M. Gelfond and V. Lifschitz. Classical Negation in Logic Programs and Disjunctive Databases. New Generation Computing, 9:365-385, 1991.
24. J. Y. Halpern. Reasoning about uncertainty. The MIT Press, 2005.
25. D. Kahneman, P. Slovic, and A. Tversky. Judgment under uncertainty: Heuristics and biases. Cambridge Univertisy Press, 1982.
26. G. Kern-Isberner and T. Lukasiewicz. Combining probabilistic logic programming with the power of maximum entropy. Artificial Intelligence, 157(1-2):139-202, 2004.
27. M. Kifer and V. S. Subrahmanian. Theory of generalized annotated logic programming and its applications. J. Log. Program., 12(3&4):335-367, 1992.
28. P. Krause, S. Ambler, M. Elvang-Gøransson, and J. Fox. A logic of argumentation for reasoning under uncertainty. Computational Intelligence, 11:113-131, 1995.
29. L. V. S. Lakshmanan. An epistemic foundation for logic programming with uncertainty. In FSTTCS, pages 89-100, 1994.
30. T. Lukasiewicz. Probabilistic logic programming. In ECAI, pages 388-392, 1998.
31. J. McCarthy and P. J. Hayes. Some philosophical problems from the standpoint of artificial intelligence. In B. Meltzer and D. Michie, editors, Machine Intelligence 4, pages 463-502. Edinburgh University Press, 1969. reprinted in McC90.
32. E. Mendelson. Introduction to Mathematical Logic. Chapman and Hall/CRC, Fourth edition 1997.
33. R. T. Ng and V. S. Subrahmanian. Probabilistic logic programming. Inf. Comput., 101(2):150-201, 1992.
34. P. Nicolas, L. Garcia, I. Stéphan, and C. Lafèvre. Possibilistic Uncertainty Handling for Answer Set Programming. Annal of Mathematics and Artificial Intelligence, 47(1-2):139-181, June 2006.
35. D. V. Nieuwenborgh, M. D. Cock, and D. Vermeir. An introduction to fuzzy answer set programming. Ann. Math. Artif. Intell., 50(3-4):363-388, 2007.
36. J. C. Nieves. Modeling arguments and uncertain information - A non-monotonic reasoning approach. PhD thesis, Software Department (LSI), Technical University of Catalonia, 2008.
37. J. C. Nieves, M. Osorio, and U. Cortés. Supporting decision making in organ transplating using argumentation theory. In LANMR 2006: 2nd Latin American Non-Monotonic Reasoning Workshop, pages 9-14, 2006.
38. J. C. Nieves, M. Osorio, and U. Cortés. Semantics for possibilistic disjunctive programs. In S. Costantini and R. Watson, editors, Answer Set Programming: Advances in Theory and Implementation (ICLP-07 Workshop), pages 271-284, 2007.
39. J. C. Nieves, M. Osorio, and U. Cortés. Semantics for possibilistic disjunctive programs (poster). In C. Baral, G. Brewka, and J. Schlipf, editors, Ninth International Conference on Logic Programming and Nonmonotonic Reasoning (LPNMR-07), number 4483 in LNAI, pages 315-320. Springer-Verlag, 2007.
40. M. Osorio and J. C. Nieves. Pstable semantics for possibilistic logic programs. In MICAI 2007: Advances in Artificial Intelligence, 6th Mexican International Conference on Artificial Intelligence, number 4827 in LNAI, pages 294-304. Springer-Verlag, 2007.
41. F. J. Pelletier and R. Elio. Scope of Logic, Methodology and Philosophy of Science, volume 1 of Synthese Library, chapter Logic and Computation, pages 137-156. Dordrecht: Kluwer Academic Press, 2002.
42. M. Rodríguez-Artalejo and C. A. Romero-Dáz. Quantitative Logic Programming revisited. In J. Garrigue and M. Hermenegildo, editors, 9th International Symposium, FLOPS, volume 4989 of LNCS, pages 272-288. Springer-Verlag Berlin Heidelberg, 2008.
43. S. SMODELS. Helsinki University of Technology. http://www.tcs.hut.fi/ Software/smodels/, 1995.
44. V. S. Subrahmanian. Uncertainty in logic programming. Association for Logic Programming (ALP), Newsletter, 20(2), May/June 2007.
45. P. Tolchinsky, U. Cortés, S. Modgil, F. Caballero, and A. López-Navidad. Increasing Human-Organ Transplant Availability: Argumentation-Based Agent Deliberation. IEEE Intelligent Systems: Special Issue on Intelligent Agents in Healthcare, 21(5):30-37, November/December 2006.
46. P. Tolchinsky, U. Cortés, J. C. Nieves, A. López-Navidad, and F. Caballero. Using arguing agents to increase the human organ pool for transplantation. In Proc. of the Third Workshop on Agents Applied in Health Care (IJCAI 2005), 2005.
47. S. E. Toulmin. The Uses of Argument. Cambridge University Press, 1958.
48. M. H. van Emden. Quantitative deduction and its fixpoint theory. Journal of Logic Programming, 3(1):37-53, 1986.