Proceedings

Extending AGM Contraction to Arbitrary Logics

Download BibTeX Cite

Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence. Special Track on Knowledge Representation and Reasoning. Pages 3299-3306.

© 2015 International Joint Conferences on Artificial Intelligence

Abstract

Classic entrenchment-based contraction is not applicable to many useful logics, such as description logics. This is because the semantic construction refers to arbitrary disjunctions of formulas, while many logics do not fully support disjunction. In this paper, we present a new entrenchment-based contraction which does not rely on any logical connectives except conjunction. This contraction is applicable to all fragments of first-order logic that support conjunction. We provide a representation theorem for the contraction which shows that it satisfies all the AGM postulates except for the controversial Recovery Postulate, and is a natural generalisation of entrenchment-based contraction.

  1. [Alchourrón et al., 1985] Carlos E. Alchourrón, Peter Gärdenfors, and David Makinson. On the logic of theory change: Partial meet contraction and revision functions. The Journal of Symbolic Logic, 50(2):510-530, 1985.
  2. [Baader et al., 2003] F. Baader, D. Calvanese, D. McGuin- ness, D. Nardi, and P. Patel-Schneider, editors. The De- scription Logic Handbook. CUP, Cambridge, UK, 2003.
  3. [Baader et al., 2005] Franz Baader, Sebastian Brand, and Carsten Lutz. Pushing the el envelope. In Proceedings of the Nineteenth International Joint Conference on Arti- ficial Intelligence(IJCAI 2005), pages 364-369.
  4. [Booth et al., 2011] Richard Booth, Thomas Meyer, Ivan José Varzinczak, and Renata Wassermann. On the link between partial meet, kernel, and infra contraction and its application to Horn logic. Journal of Artificial Intelligence Research, 42:31-53, 2011.
  5. [Creignou et al., 2014] Nadia Creignou, Odile Papini, Rein- hard Pichler, and Stefan Woltran. Belief revision within fragments of propositional logic. Journal of Computer and System Sciences, 80(2):427-449, 2014.
  6. [Delgrande and Peppas, 2015] James P. Delgrande and Pav- los Peppas. Belief revision in Horn theories. Artificial Intelligence, 218:1-22, 2015.
  7. [Delgrande and Wassermann, 2013] James P. Delgrande and Renata Wassermann. Horn clause contraction functions. Journal of Artificial Intelligence Research, 48:475-551, 2013.
  8. [Fermé et al., 2008] Eduardo Fermé, Martín Krevneris, and Maurício Reis. An axiomatic characterization of ensconcement-based contraction. Journal of Logic and Computation, 18(5):739-753, 2008.
  9. [Gärdenfors and Makinson, 1988] Peter Gärdenfors and David Makinson. Revisions of knowledge systems using epistemic entrenchment. In Proceedings of the 2nd conference on Theoretical Aspects of Reasoning about Knowledge (TARK-1988), pages 83-95, 1988.
  10. [Gärdenfors, 1988] Peter Gärdenfors. Knowledge in Flux: Modelling the Dynamics of Epistemic States. MIT Press, 1988.
  11. [Hansson, 1991] Sven Ove Hansson. Belief Contraction Without Recovery. Studia Logica, 50(2):251-260, 1991.
  12. [Hansson, 1993] Sven Ove Hansson. Changes of disjunc- tively closed bases. Journal of Logic, Language and In- formation, 2(4):255-284, 1993.
  13. [Horrocks et al., 2005] Ian Horrocks, Oliver Kutz, and Ul- rike Sattler. The irresistible SRIQ. In In Proc. of OWL: Experiences and Directions, 2005.
  14. [Kautz and Selman, 1996] Henry Kautz and Bart Selman. Knowledge compilation and theory approximation. Jour- nal of the ACM, 43:193-224, 1996.
  15. [Levi, 1991] Issac Levi. The Fixation of Beliefs and its Udo- ing. Cambridge University Press, 1991.
  16. [Makinson, 1987] David Makinson. On the status of the pos- tulate of recovery in the logic of theory change. Journal of Philosophical Logic, 16(4):383-394, 1987.
  17. [Ortiz et al., 2010] Magdalena Ortiz, Sebastian Rudolph, and Mantas Simkus. Worst-case optimal reasoning for the Horn-DL fragments of OWL 1 and 2. In Proceedings of the Twelfth International Conference on Principles of Knowl- edge Representation and Reasoning, (KR-2010), 2010.
  18. [Poggi et al., 2008] Antonella Poggi, Domenico Lembo, Diego Calvanese, Giuseppe De Giacomo, Maurizio Lenz- erini, and Riccardo Rosati. Journal on data seman- tics. chapter Linking Data to Ontologies, pages 133-173. Springer-Verlag, 2008.
  19. [Qi and Du, 2009] Guilin Qi and Jianfeng Du. Model-based revision operators for terminologies in description logics. In Proc. IJCAI-2009, pages 891-897, 2009. IJCAI
  20. [Qi et al., 2006] Guilin Qi, Weiru Liu, and David A. Bell. Knowledge base revision in description logics. In Pro- ceedings of the 10th European Conference on Logics in Ar- tificial Intelligence (JELIA-2006), pages 386-398, 2006.
  21. [Qi et al., 2008] Guilin Qi, Peter Haase, Zhisheng Huang, Qiu Ji, Jeff Z. Pan, and Johanna Volker. A kernel revision operator for terminologies algorithms and evaluation. In Proceedings of the 7th International Semantic Web Con- ference (ISWC-2008), pages 419-434, 2008.
  22. [Ribeiro and Wassermann, 2009] Márcio M. Ribeiro and Re- nata Wassermann. Base revision for ontology debugging. Journal of Logic Computation, 19(5):721-743, 2009.
  23. [Ribeiro et al., 2013] Márcio M. Ribeiro, Renata Wasser- mann, Giorgos Flouris, and Grigoris Antoniou. Minimal change: Relevance and recovery revisited. Artificial Intel- ligence, 201:59-80, 2013.
  24. [Rott, 1992] Hans Rott. Preferential belief change using gen- eralised epistemic entrenchment. Journal of Logic, Lan- guage and Information, 1(1):45-78, 1992.
  25. [Wang et al., 2010] Zhe Wang, Kewen Wang, and Rodney Topor. A new approach to knowledge base revision in DL- Lite. In Proc. AAAI-2010, 2010.
  26. [Zhuang and Pagnucco, 2014] Zhiqiang Zhuang and Mau- rice Pagnucco. Entrenchment-based Horn contraction. Journal of Artificial Intelligence Research, 51:227-254, 2014.
  27. [Zhuang et al., 2014] Zhiqiang Zhuang, Zhe Wang, Kewen Wang, and Guilin Qi. Contraction and revision over DL- Lite tboxes. In Proceedings of the 28th AAAI Conference (AAAI-2014), 2014.

References were extracted automatically from the PDF and may contain errors.

Welcome Future Conferences AI Journal Division IJCAI Proceedings About IJCAI IJCAI Awards