Skip to main content
Log in

Prime forms and minimal change in propositional belief bases

  • Published:
Annals of Mathematics and Artificial Intelligence Aims and scope Submit manuscript

Abstract

This paper proposes to use prime implicants and prime implicates normal forms to represent belief sets. This representation is used, on the one hand, to define syntactical versions of belief change operators that also satisfy the rationality postulates but present better complexity properties than those proposed in the literature and, on the other hand, to propose a new minimal distance that adopts as a minimal belief unit a “fact”, defined as a prime implicate of the belief set, instead of the usually adopted Hamming distance, i.e., the number of propositional symbols on which the models differ. Some experiments are also presented that show that this new minimal distance allows to define belief change operators that usually preserve more information of the original belief set.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Alchourrón, C., Gärdenfors, P., Makinson, D.: On the logic of theory change: partial meet functions for contraction and revision. J. Symb. Log. 50, 510–530 (1985)

    Article  MATH  Google Scholar 

  2. Bienvenu, M.: Prime implicates and prime implicants: from propositional to modal logic. J. Artif. Intell. Res. 36, 71–128 (2009)

    MATH  Google Scholar 

  3. Bienvenu, M., Herzig, A., Qi, G.: Prime implicate-based belief revision operators. In: Proc. of ECAI’08 (2008)

  4. Bittencourt, G.: Advances in modeling adaptive and cognitive systems. In: Chap. A Memory Model for Cognitive Agents, pp. 60–76. UEFS (2010). ISBN 978-85-7395-194-3. http://www2.uefs.br/graco/amacs/

  5. Bittencourt, G., Marchi, J.: Propositional reasoning for an embodied cognitive model. In: Bazzan, A.L.C., Labidi, S. (eds.) Proc. of the 17th Brazilian Symposium on Artificial Intelligence (SBIA’04), pp. 164–173. Springer, São Luís, Maranhão, Brasil (2004)

    Google Scholar 

  6. Bittencourt, G., Marchi, J.: Artificial cognition systems. In: Chap. An Embodied Logical Model for Cognition, pp. 27–63. IDEA Group Inc (2006)

  7. Bittencourt, G., Marchi, J., Padilha, R.S.: A syntactic approach to satisfaction. In: Konev, B., Schimidt, R. (eds.) 4th Inter. Workshop on the Implementation of Logic (LPAR03), pp. 18–32. Univ. of Liverpool and Univ. of Manchester (2003)

  8. Bittencourt, G., Perrussel, L., Marchi, J.: A syntactical approach to revision. In: Mántaras, R.L., Saitta, L. (eds.) Proc. of the 16th Europ. Conf. on Artificial Intelligence (ECAI’04), pp. 788–792. IOS Press, Valencia, Spain (2004)

    Google Scholar 

  9. Boutilier, C.: A unified model of qualitative belief change: a dynamical systems perspective. Artif. Intell. 98(1–2), 281–316 (1998). citeseer.ist.psu.edu/boutilier98unified.html

    Article  MATH  MathSciNet  Google Scholar 

  10. Cadoli, M., Donini, F.M.: A survey on knowledge compilation. AI Commun. 10(3–4), 137–150 (1997). citeseer.ist.psu.edu/cadoli98survey.html

    Google Scholar 

  11. Dalal, M.: Investigations into a theory of knowledge base revision: preliminary report. In: Rosenbloom, P., Szolovits, P. (eds.) Proceedings of the 7th National Conf. on Artificial Intelligence, vol. 2, pp. 475–479. AAAI Press, Menlo Park, California (1988). citeseer.nj.nec.com/dalal88investigations.html

    Google Scholar 

  12. Darwich, A., Marquis, P.: A knowledge compilation map. J. Artif. Intell. Res. 17, 229–264 (2002)

    Google Scholar 

  13. Darwiche, A., Marquis, P.: A perspective on knowledge compilation. In: IJCAI, pp. 175–182 (2001). citeseer.nj.nec.com/darwiche01perspective.html

  14. del Val, A.: Syntactic characterizations of belief change operators. In: Bajcsy, R. (ed.) Proceedings of the 13th International Joint Conference on Artificial Intelligence (IJCAI’93), Chambéry, France, pp. 540–547. Morgan Kaufmann (1993)

  15. Delgrande, J.P., Nayak, A.C., Pagnucco, M.: Conservative belief revision. In: McGuinness, D.L., Ferguson, G. (eds.) Proceedings of the Nineteenth National Conference on Artificial Intelligence, Sixteenth Conference on Innovative Applications of Artificial Intelligence, July 25–29, 2004, San Jose, California, USA, pp. 251–256. AAAI Press/The MIT Press (2004)

  16. Doyle, J.: Rational belief revision. In: Proceedings of the 2nd International Conference on Principles of Knowledge Representation and Reasoning (KR’91), Cambridge, MA, USA, April 22–25, 1991, pp. 163–174. Morgan Kaufmann Publishers (1991)

  17. Fitting, M.: First-Order Logic and Automated Theorem Proving. Springer, New York (1990)

    MATH  Google Scholar 

  18. Forbus, K.: Introducing actions into qualitative simulation. In: Proceedings IJCAI-89, pp. 1273–1278. Detroit, MI (1989)

  19. Friedman, N., Halpern, J.Y.: A knowledge-based framework for belief change, part I: foundations. In: Fagin, R. (ed.) Theoretical Aspects of Reasoning about Knowledge: Proc. 5th Conference, pp. 44–64 (1994)

  20. Friedman, N., Halpern, J.Y.: A knowledge-based framework for belief change, part II: revision and update. In: Doyle, J., Sandewall, E., Torasso, P. (eds.) KR’94: Principles of Knowledge Representation and Reasoning, pp. 190–201. Morgan Kaufmann, San Francisco, California (1994). citeseer.nj.nec.com/friedman94knowledgebased.html

    Google Scholar 

  21. Gärdenfors, P.: Knowledge in Flux: Modelling the Dynamics of Epistemic States. Bradford Books, MIT Press (1988)

  22. Gorogiannis, N., Ryan, M.: Implementation of belief change operators using bdds. Stud. Log. 70(1), 131–156 (2004)

    Article  MathSciNet  Google Scholar 

  23. Grove, A.: Two modellings for theory change. J. Philos. Logic 17, 157–170 (1988)

    Article  MATH  MathSciNet  Google Scholar 

  24. Hansson, S.: A Textbook of Belief Dynamics. Theory Change and Database Updating. Kluwer (1999)

  25. Herzig, A., Rifi, O.: Update operations: a review. In: Prade, H. (ed.) Proc. of the 13th European Conf. on Artificial Intelligence (ECAI’98), pp. 13–17. Wiley, Chichester (1998). citeseer.ist.psu.edu/herzig98update.html

    Google Scholar 

  26. Herzig, A., Rifi, O.: Propositional belief base update and minimal change. Artif. Intell. 115(1), 107–138 (1999). citeseer.nj.nec.com/herzig99propositional.html

    Article  MATH  MathSciNet  Google Scholar 

  27. Jackson, P.: Computing prime implicants. In: Proceedings of the 10th International Conference on Automatic Deduction, Kaiserslautern, Germany. LNAI no. 449, pp. 543–557. Springer (1990)

  28. Katsuno, H., Mendelzon, A.: On the difference between updating a knowledge base and revising it. In: Allen, J.F., Fikes, R., Sandewall, E. (eds.) KR’91: Principles of Knowledge Representation and Reasoning, pp. 387–394. Morgan Kaufmann, San Mateo, California (1991). citeseer.nj.nec.com/417296.html

    Google Scholar 

  29. Katsuno, H., Mendelzon, A.: Propositional knowledge base revision and minimal change. Artif. Intell. 52(3), 263–294 (1991)

    Article  MATH  MathSciNet  Google Scholar 

  30. Katsuno, H., Mendelzon, A.O.: A unified view of propositional knowledge base updates. In: Proceedings of the 11th International Joint Conference on Artificial Intelligence (IJCAI’89), Detroit, MI, USA, August 1989, pp. 1413–1419. Morgan Kaufmann (1989)

  31. Kean, A., Tsiknis, G.: An incremental method for generating prime implicants/implicates. J. Symb. Comput. 9, 185–206 (1990)

    Article  MATH  MathSciNet  Google Scholar 

  32. Konieczny, S., Pérez, R.P.: Propositional belief base merging or how to merge beliefs/goals coming from several sources and some links with social choice theory. Eur. J. Oper. Res. 160(3), 785–802 (2005)

    Article  MATH  Google Scholar 

  33. Liberatore, P., Schaerf, M.: Arbitration (or how to merge knowledge bases). IEEE Trans. Knowl. Data Eng. 10(1), 76–90 (1998)

    Article  Google Scholar 

  34. Makinson, D.: Propositional relevance through letter-sharing. Journal of Applied Logic 7, 377–387 (2009)

    Article  MATH  MathSciNet  Google Scholar 

  35. Manquinho, V.M., Flores, P.F., Marques-Silva, J.P., Oliveira, A.L.: Prime implicant computation using satisfiability algorithms. In: Proceedings of the IEEE International Conference on Tools with Artificial Intelligence (ICTAI’97), pp. 232–239. IEEE (1997)

  36. Marchi, J., Bittencourt, G., Perrussel, L.: A syntactical approach to update. In: Proc. of Mexican International Conf. on Artificial Intelligence (MICAI’05). Springer, Monterrey, Mexico (2005)

    Google Scholar 

  37. Marchi, J., Bittencourt, G., Perrussel, L.: Perspectives on universal logic. In: Chap. Prime Forms and Belief Revision, pp. 365–377. Polimetrica (2007)

  38. Morgan, C.G.: Probability, rational belief and belief change. In: Delgrande, J.P., Schaub, T. (eds.) 10th International Workshop on Non-Monotonic Reasoning (NMR 2004), Whistler, Canada, June 6–8, 2004, Proceedings, pp. 297–305 (2004)

  39. Nebel, B.: A knowledge level analysis of belief revision. In: Principles of Knowledge Representation and Reasoning: Proceedings of the 1st International Conference (KR’89), pp. 301–311 (1989)

  40. Nebel, B.: Belief revision and default reasoning: syntax-based approaches. In: Allen, J.A., Fikes, R., Sandewall, E. (eds.) Principles of Knowledge Representation and Reasoning: Proceedings of the 2nd International Conference, pp. 417–428. Morgan Kaufmann, San Mateo (1991). citeseer.ist.psu.edu/nebel91belief.html

    Google Scholar 

  41. Nebel, B.: Base revision operations and schemes: semantics, representation, and complexity. In: Proceedings of the 11th European Conference on Artificial Intelligence (ECAI’94), pp. 341–345 (1994)

  42. Pagnucco, M.: The Role of Abductive Reasoning Within the Process of Belief Revision. Ph.D. Thesis, Department of Computer Science, University of Sydney (1996)

  43. Pagnucco, M.: Knowledge compilation for belief change. In: Proceedings of the 19th Australian Joint Conference on Artificial Intelligence (AI06). Lecture Notes in Artificial Intelligence, vol. 4304, pp. 90–99. Springer (2006)

  44. Parikh, R.: Beliefs, Belief Revision, and Splitting Languages, vol. 2, pp. 266–278. Center for the Study of Language and Information, Stanford, CA, USA (1999)

  45. Perrussel, L., Marchi, J., Bittencourt, G.: Quantum-based belief merging. In: Proceedings of the 11th Ibero-American Conference on AI (IBERAMIA’08). Lecture Notes in Computer Science, vol. 5290, pp. 21–30. Springer (2008)

  46. Ramesh, A., Becker, G., Murray, N.V.: CNF and DNF considered harmful for computing prime implicants/implicates. J. Autom. Reason. 18(3), 337–356 (1997). citeseer.nj.nec.com/516217.html

    Article  MATH  MathSciNet  Google Scholar 

  47. Revesz, P.Z.: On the semantics of arbitration. Int. J. Algebra Comput. 7(2), 133–160 (1995)

    Article  MathSciNet  Google Scholar 

  48. Satoh, K.: Nonmonotonic reasoning by minimal belief revision. In: FGCS, pp. 455–462 (1988)

  49. Schrag, R., Crawford, J.M.: Implicates and prime implicates in random 3-SAT. Artif. Intell. 81(1–2), 199–222 (1995). citeseer.ist.psu.edu/article/schrag95implicate.html

    MathSciNet  Google Scholar 

  50. Sloan, R.H., Szörényi, B., Turán, G.: On k-term dnf with the largest number of prime implicants. SIAM J. Discrete Math. 21(4), 987–998 (2008). doi:10.1137/050632026

    Article  Google Scholar 

  51. Socher, R.: Optimizing the clausal normal form transformation. J. Autom. Reason. 7(3), 325–336 (1991)

    Article  MATH  MathSciNet  Google Scholar 

  52. Winslett, M.: Reasoning about action using a possible models approach. In: Proceedings of the 7th National Conf. on Artificial Intelligence, pp. 89–93 (1988)

  53. Zhuang, Z., Pagnucco, M., Meyer, T.: Implementing iterated belief change via prime implicates. In: Proceeding of the 20th Australian Joint Conference on Artificial Intelligence, pp. 507–518 (2007)

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jerusa Marchi.

Additional information

In memory of G. Bittencourt.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Marchi, J., Bittencourt, G. & Perrussel, L. Prime forms and minimal change in propositional belief bases. Ann Math Artif Intell 59, 1–45 (2010). https://doi.org/10.1007/s10472-010-9206-x

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10472-010-9206-x

Keywords

Mathematics Subject Classifications (2010)

Navigation