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Stratified Belief Bases Revision with Argumentative Inference

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Abstract

We propose a revision operator on a stratified belief base, i.e., a belief base that stores beliefs in different strata corresponding to the value an agent assigns to these beliefs. Furthermore, the operator will be defined as to perform the revision in such a way that information is never lost upon revision but stored in a stratum or layer containing information perceived as having a lower value. In this manner, if the revision of one layer leads to the rejection of some information to maintain consistency, instead of being withdrawn it will be kept and introduced in a different layer with lower value. Throughout this development we will follow the principle of minimal change, being one of the important principles proposed in belief change theory, particularly emphasized in the AGM model. Regarding the reasoning part from the stratified belief base, the agent will obtain the inferences using an argumentative formalism. Thus, the argumentation framework will decide which information prevails when sentences of different layers are used for entailing conflicting beliefs. We will also illustrate how inferences are changed and how the status of arguments can be modified after a revision process.

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References

  1. Alchourrón, C., Gärdenfors, P., & Makinson, D. (1985). On the logic of theory change: Partial meet contraction and revision functions. The Journal of Symbolic Logic, 50, 510–530.

    Article  Google Scholar 

  2. Alchourrón, C., & Makinson, D. (1985). On the logic of theory change: Safe contraction. Studia Logica, 44, 405–422.

    Article  Google Scholar 

  3. Baroni, P., & Giacomin, M. (2007). Comparing argumentation semantics with respect to skepticism. In 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, (ECSQARU 2007) (pp. 210–221). Tunisia: Hammamet.

    Chapter  Google Scholar 

  4. Baroni, P., & Giacomin, M. (2009). Semantics of abstract argument systems. In I. Rahwan, & G. R. Simari (Eds.), Argumentation in Artificial Intelligence, (pp. 24–44). Springer.

  5. Baroni, P., & Giacomin, M. (2009). Skepticism relations for comparing argumentation semantics. International Journal Approximate Reasoning, 50(6), 854–866.

    Article  Google Scholar 

  6. Bench-Capon, T. J. M., & Dunne, P. E. (2007). Argumentation in artificial intelligence. Artificial Intelligence, 171(10–15), 619–641.

    Article  Google Scholar 

  7. Benferhat, S., Dubois, D., & Prade, H. (1993). Argumentative inference in uncertain and inconsistent knowledge bases. In D. Heckerman, & E. H. Mamdani (Eds.), Proceedings 9th Annual Conference on Uncertainty in Artificial Intelligence, UAI’1993 (pp. 411–419). Morgan Kaufmann.

  8. Benferhat, S., Dubois, D., & Prade, H. (1995). How to infer from inconsistent beliefs without revising. In Proceedings of IJCAI’1995 (pp. 1449–1455).

  9. Benferhat, S., Kaci, S., Berre, D. L., & Williams, M.-A. (2004). Weakening conflicting information for iterated revision and knowledge integration. Artificial Intelligence Journal, 153(1–2), 339–371.

    Article  Google Scholar 

  10. Besnard, P., & Hunter, A. (2001). A logic-based theory of deductive arguments. Artificial Intelligence, 128(1–2), 203–235.

    Article  Google Scholar 

  11. Besnard, P., & Hunter, A. (2009). Argumentation based on classical logic. In I. Rahwan, & G. R. Simari (Eds.), Argumentation in Artificial Intelligence, (pp. 133–152). Springer.

  12. Boella, G., Costa Perera, C. d., Tettamanzi, A., & van der Torre, L. (2008). Dung argumentation and AGM belief revision. In 5th International Workshop on Argumentation in Multi-Agent Systems, ArgMAS’2008.

  13. Boella, G., Costa Perera, C. d., Tettamanzi, A., & van der Torre, L. (2008). Making others believe what they want. Artificial Intelligence in Theory and Practice II (pp. 215–224).

  14. Brewka, G. (1989). Belief revision in a framework for default reasoning. In A. Fuhrmann, & M. Morreau (Eds.), The logic of theory change. Lecture Notes in Computer Science (Vol. 465, pp. 206–222). Springer.

  15. Brewka, G. (1989). Preferred subtheories: An extended logical framework for default reasoning. In Proceedings 11th Joint Conference on Artificial Intelligence, IJCAI 1989 (Vol. 2, pp. 1043–1048). San Mateo, California.

  16. Cantwell, J. (1998). Resolving conflicting information. Journal of Logic, Language and Information, 7(2), 191–220.

    Article  Google Scholar 

  17. Cayrol, C., de Saint Cyr, F. D., & Lagasquie Schiex, M. C. (2008). Revision of an argumentation system. In Proceedings of the International Conference on Principles of Knowledge Representation and reasoning, KR 2008 (pp. 124–134).

  18. Darwiche, A., & Pearl, J. (1997). On the logic of iterated belief revision. Artificial Intelligence, 89, 1–29.

    Article  Google Scholar 

  19. Delgrande, J., Dubois, D., & Lang, J. (2006). Iterated revision and prioritized merging. In Proceedings of KR 2006 (pp. 210–220). AAAI Press.

  20. Delgrande, J., Schaub, T., Tompits, H., & Woltran, S. (2008). Belief revision of logic programs under answer set semantics. In Proceedings of the 11th International Conference on Principles of Knowledge Representation and Reasoning (pp. 411–421).

  21. Delgrande, J. P., Schaub, T., Tompits, H., & Woltran, S. (2008). Belief revision of logic programs under answer set semantics. In G. Brewka, & J. Lang (Eds.), KR (pp. 411–421). AAAI Press.

  22. Doyle, J. (1979). A truth maintenance system. Artificial Intelligence, 12, 231–272.

    Article  Google Scholar 

  23. Doyle, J. (1992). Reason maintenance and belief revision: Foundations versus coherence theories. In P. Gärdenfors (Ed.), Belief revision, (pp. 29–51). Cambridge University Press.

  24. Dubois, D. (2006). Three scenarios for the revision of epistemic states. In Proceedings of NMR’2006.

  25. 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.

    Article  Google Scholar 

  26. 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, 321–357.

    Article  Google Scholar 

  27. Falappa, M. A., Fermé, E. L., & Kern-Isberner, G. (2006). On the logic of theory change: Relations between incision and selection functions. In Proceedings of ECAI-2006 (pp. 402–406).

  28. Falappa, M. A., Kern-Isberner, G., Reis, M. D. L., & Simari, G. R. (2011). Prioritized and non-prioritized multiple change on belief bases. The Journal of Philosophical Logic. doi:10.1007/s10992-011-9200-8

  29. Falappa, M. A., Kern-Isberner, G., & Simari, G. R. (2002). Belief revision, explanations and defeasible reasoning. Artificial Intelligence Journal, 141, 1–28.

    Article  Google Scholar 

  30. Falappa, M. A., Kern-Isberner, G., & Simari, G. R. (2009) Argumentation in Artificial Intelligence. In I. Rahwan, & G. R. Simari (Eds.), Chapt. Belief Revision and Argumentation Theory (pp. 341–360). Springer.

  31. Fuhrmann, A., & Hansson, S. O. (1994). A survey of multiple contractions. The Journal of Logic, Language and Information, 3, 39–76.

    Article  Google Scholar 

  32. García, A. J., & Simari, G. R. (2004). Defeasible logic programming: An argumentative approach. Theory and Practice of Logic Programming, 4(1), 95–138.

    Article  Google Scholar 

  33. Gärdenfors, P. (1988). Knowledge in flux: Modelling the dynamics of epistemic states. The MIT Press, Bradford Books: Cambridge.

    Google Scholar 

  34. Gärdenfors, P. (1990). The dynamics of belief systems: Foundations vs. coherence theories. Revue Internationale of Philosophie, 44, 24–46.

    Google Scholar 

  35. Gärdenfors, P., & Makinson, D. (1988). Revisions of knowledge systems using epistemic entrenchment. 2nd conference on theoretical aspects of reasoning about knowledge (pp. 83–95).

  36. Gómez Lucero, M. J., Chesñevar, C. I., & Simari, G. R. (2009). Modelling argument accrual in possibilistic defeasible logic programming. In Proceedings of ECSQARU’2009 (pp. 131–143).

  37. Gómez Lucero, M. J., Chesñevar, C. I., & Simari, G. R. (2009). On the accrual of arguments in defeasible logic programming. In Proceedings of IJCAI’2009 (pp. 804–809).

  38. Grove, A. (1988). Two modellings for theory change. The Journal of Philosophical Logic, 17, 157–170.

    Article  Google Scholar 

  39. Hansson, S. O. (1993). Reversing the levi identity. The Journal of Philosophical Logic, 22, 637–669.

    Article  Google Scholar 

  40. Hansson, S. O. (1994). Kernel contraction. The Journal of Symbolic Logic, 59, 845–859.

    Article  Google Scholar 

  41. Hansson, S. O. (1999). A textbook of belief dymanics: Theory change and database updating. Kluwer Academic Publishers.

  42. Kern-Isberner, G. (1999). Postulates for conditional belief revision. In Proceedings of 16th International Joint Conference on Artificial Intelligence, IJCAI 1999 (pp. 186–191). Morgan Kaufmann.

  43. Kern-Isberner, G. (2004). A thorough axiomatization of a principle of conditional preservation in belief revision. Annals of Mathematics and Artificial Intelligence, 40(1–2), 127–164.

    Article  Google Scholar 

  44. Kern-Isberner, G. (2008). Linking iterated belief change operations to nonmonotonic reasoning. In G. Brewka, & J. Lang (Eds.), Proceedings of 11th International Conference on Knowledge Representation and Reasoning, KR 2008 (pp. 166–176). Menlo Park: AAAI Press.

    Google Scholar 

  45. Levi, I. (1977). Subjunctives, dispositions, and chances. Synth̀ese, 34, 423–455.

    Article  Google Scholar 

  46. Lindström, S. & Rabinowicz, W. (1989). Epistemic entrenchment with incomparabilities and relational belief revision. In In A. Fuhrmann, & M. Morreau (eds.), The logic of theory change. Lecture Notes in Computer Science (pp. 93–126). Springer

  47. Makinson, D., & Gärdenfors, P. (1991). Relations between the logic of theory change and nonmonotonic logic. Lecture Notes in Computer Science, 465, 183–205.

    Article  Google Scholar 

  48. Moguillansky, M. O., Rotstein, N. D., Falappa, M. A., García, A. J., & Simari, G. R. (2008). Argument theory change applied to defeasible logic programming. In Proceedings of the 23rd Conference on Artificial Intelligence, AAAI 2008 (pp. 132–137).

  49. Moguillansky, M. O., Rotstein, N. D., Falappa, M. A., García, A. J., & Simari, G. R. (2011). Argument Theory change applied to defeasible logic programming. Theory and Practice of Logic Programming (TPLP), (in press)

  50. Paglieri, F., & Castelfranchi, C. (2006). The toulmin test: Framing argumentation within belief revision theories (pp. 359–377). Berlin: Springer.

    Google Scholar 

  51. Peppas, P. (2004). The limit assumption and multiple revision. Journal of Logic and Computation, 14(3), 355–371.

    Article  Google Scholar 

  52. Pollock, J. L. (1994). Justification and defeat. Artificial Intelligence, 67, 377–407.

    Article  Google Scholar 

  53. Pollock, J. L., & Gillies, A. S. (2000). Belief revision and epistemology. Synthese, 122(1–2), 69–92.

    Article  Google Scholar 

  54. Prakken, H. (1993). Logical tools for modelling legal argument. Ph.D. thesis, Vrije Universiteit, Amsterdan.

  55. Rahwan, I., & Simari, G. R. (2009). Argumentation in artificial intelligence. Springer.

  56. Rotstein, N. D., Moguillansky, M. O., Falappa, M. A., García, A. J., & Simari, G. R. (2008). Argument theory change: Revision upon warrant. In Proceedings of the Computational Models of Argument, COMMA’2008 (pp. 336–347).

  57. Rott, H. (1992). Preferential belief change using generalized epistemic entrenchment. The Journal of Logic, Language and Information, 1, 45–78.

    Google Scholar 

  58. Simari, G. R., & Loui, R. P. (1992). A mathematical treatment of defeasible reasoning and its implementation. Artificial Intelligence, 53, 125–157.

    Article  Google Scholar 

  59. Tamminga, A. (2004). Expansion and contraction of finite states. Studia Logica, 76(3), 427–442.

    Article  Google Scholar 

  60. Verheij, B. (1995). Accrual of arguments in defeasible argumentation. In Proceedings of the 2nd Dutch/German Workshop on Nonmonotonic Reasoning, (pp. 217–224). Utrecht.

  61. Williams, M.-A. (1994). Transmutations of knowledge systems. In J. Doyle, E. Sandewall, & P. Torasso (Eds.), Proceedings of the 4th International Conference on Principles of Knowledge Representation and Reasoning (KR 1994), (pp. 619–629). Bonn, Germany.

  62. Williams, M.-A. (1996). Towards a practical approach to belief revision: Reason-based change. In L. C. Aiello, J. Doyle, & S. C. Shapiro (Eds.), Proceedings of the 5th International Conference on Principles of Knowledge Representation and Reasoning (KR 1996), (pp. 412–420). Cambridge, MA.

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Correspondence to Marcelo Alejandro Falappa.

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Falappa, M.A., García, A.J., Kern-Isberner, G. et al. Stratified Belief Bases Revision with Argumentative Inference. J Philos Logic 42, 161–193 (2013). https://doi.org/10.1007/s10992-011-9217-z

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