About Handling Non-conflicting Additional Information

  • Éric Grégoire
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 263)


The focus in this chapter is on logic-based Artificial Intelligence (A.I.) systems that must accommodate some incoming symbolic knowledge that is not inconsistent with the initial beliefs but that however requires a form of belief change. First, we investigate situations where the incoming knowledge is both more informative and deductively follows from the preexisting beliefs: the system must get rid of the existing logically subsuming information. Likewise, we consider situations where the new knowledge must replace or amend some previous beliefs. When the A.I. system is equipped with standard-logic inference capabilities, merely adding this incoming knowledge into the system is not appropriate. In the chapter, this issue is addressed within a Boolean standard-logic representation of knowledge and reasoning. Especially, we show that a prime implicates representation of beliefs is an appealing specific setting in this respect.


Artificial intelligence Knowledge representation and reasoning  Logic Belief change 


  1. 1.
    Fermé, E., Hansson, S.: AGM 25 years. Twenty-five years of research in belief change. J. Philos. Logic. 40, 295–331 (2011)Google Scholar
  2. 2.
    Alchourrón, C., Gärdenfors, P., Makinson, D.: On the logic of theory change: partial meet contraction and revision functions. J. Symb. Logic. 50(2), 510–530 (1985)CrossRefMATHGoogle Scholar
  3. 3.
    Katsuno, H., Mendelzon, A.: On the difference between updating a knowledge base and revising it. In: Proceedings of KR’91, pp. 387–394 (1991)Google Scholar
  4. 4.
    Gärdenfors, P.: Knowledge in Flux: Modeling the Dynamics of Epistemic States. MIT Press, Cambridge (1988)MATHGoogle Scholar
  5. 5.
    Hansson, S.O.: A Textbook of Belief Dynamics: Theory Change and Database Updating. Kluwer Academic Publishers, Dordrecht (1999)CrossRefMATHGoogle Scholar
  6. 6.
    Konieczny, S., Pino Pérez, R.: On the logic of merging. In: Proceedings of KR’98, pp. 488–498 (1998)Google Scholar
  7. 7.
    Konieczny, S., Grégoire, É.: Logic-based information fusion in artificial intelligence. Inf. Fusion. 7(1), 4–18 (2006)CrossRefGoogle Scholar
  8. 8.
    Doyle, J.: A truth maintenance system. Artif. Intell. 12, 231–272 (1979)CrossRefMathSciNetGoogle Scholar
  9. 9.
    Dalal, M.: Investigations into a theory of knowledge base revision (preliminary report). In: Proceedings of AAAI’88, vol. 2, pp. 475–479 (1988)Google Scholar
  10. 10.
    Revesz, P.Z.: On the semantics of theory change: arbitration between old and new information. In: Proceedings of PODS’93, pp. 71–82 (1993)Google Scholar
  11. 11.
    Subrahmanian, V.S.: Amalgamating knowledge bases. ACM Trans. Database Syst. 19, 291–331 (1994)CrossRefMathSciNetGoogle Scholar
  12. 12.
    Fagin, R., Ullman, J.D, Vardi, M.Y.: On the semantics of updates in databases. In: Proceedings of PODS’83, pp. 352–365 (1983)Google Scholar
  13. 13.
    Besnard, Ph., Grégoire, É., Ramon, S.: Enforcing logically weaker knowledge in classical logic. In: 5th International Conference on Knowledge Science Engineering and Management (KSEM’11), pp. 44–55. LNAI 7091, Springer (2011)Google Scholar
  14. 14.
    Besnard, Ph., Grégoire, É., Ramon, S.: Preemption operators. In: Proceedings of ECAI 2012, pp. 893–894 (2012)Google Scholar
  15. 15.
    Besnard, Ph., Grégoire, É., Ramon, S.: Overriding subsuming rules. In: Proceedings of ECSQARU’11, pp. 532–544. LNAI 6717, Springer (2011)Google Scholar
  16. 16.
    Besnard, Ph., Grégoire, É., Ramon, S.: Logic-based fusion of legal knowledge. In: Proceeings of Fusion 2012, pp. 587–592. IEEE Press, Singapore (2012)Google Scholar
  17. 17. satlive is a main website of the research community on SAT
  18. 18.
    Zhuang, Z.Q., Pagnucco, M., Meyer, T.: Implementing iterated belief change via prime implicates. In: Orgun, M.A., Thornton, J. (eds) Australian Conference on Artificial Intelligence, volume 4830 of Lecture Notes in Computer Science, pp. 507–518. Springer (2007)Google Scholar
  19. 19.
    Bienvenu, M., Herzig, A., Qi, G.: Prime implicate-based belief revision operators. In: 20th European Conference on Artificial Intelligence (ECAI 2012), pp. 741–742 (2008)Google Scholar
  20. 20.
    Darwiche, A., Marquis, P.: A knowledge compilation map. J. Artif. Intell. Res. (JAIR) 17, 229–264 (2002)MATHMathSciNetGoogle Scholar
  21. 21.
    Eiter, T., Makino, K.: Generating all abductive explanations for queries on propositional horn theories. In: Computer Science Logic, 17th International Workshop, CSL 2003, 12th Annual Conference of the EACSL, and 8th Kurt Gödel Colloquium, KGC 2003, Vienna, Austria, August 25–30, pp. 197–211 (2003)Google Scholar
  22. 22.
    Besnard, Ph., Grégoire, É.: Handling incoming beliefs. In: 6th International Conference on Knowledge Science Engineering and Management (KSEM’13), LNAI, Springer (2013)Google Scholar
  23. 23.
    Grégoire, É., Mazure, B., Piette, C.: Using local search to find MSSes and MUSes. Eur. J. Oper. Res. 199(3), 640–646 (2009)CrossRefMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.CRIL Université d’ArtoisLens CedexFrance

Personalised recommendations