Advertisement

A Framework for Merging Possibilistic Knowledge Bases

  • Thi Thanh Luu LeEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11432)

Abstract

Knowledge base merging is one of active research fields with a large range of applications in Artificial Intelligence. Most of the works in this research field has a lot of restrictions such as in the centralized approach, drowning effect, it is difficult to apply to interactive systems such as multi-agent systems as well as omitting knowledge in the merging process. The purpose of this paper is to focus on the integration of possibilistic knowledge bases in the propositional sense in propositional language. The integration is done through the special possibility distribution of possibilistic knowledge bases. To solve that problem, we introduce a new powerful argumentation framework for merging knowledge bases. In order to model this argument, firstly, the source knowledge bases of each agent are ranked in order of priority and the merging of prior knowledge bases into a priority knowledge base and then infer the final knowledge. An axiomatic model, including a set of rational and intuitive postulates is interested and discussed so that the merging result of knowledge bases needs to be satisfied.

Keywords

Argumentation Knowledge base merging Possibilistic logic 

References

  1. 1.
    Dung, P.M.: On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming and n-person games. Artif. Intell. 77(2), 321–357 (1995)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Benferhat, S., Dubois, D., Prade, H., Williams, M.-A.: A practical approach to fusing prioritized knowledge bases. In: Barahona, P., Alferes, J.J. (eds.) EPIA 1999. LNCS (LNAI), vol. 1695, pp. 222–236. Springer, Heidelberg (1999).  https://doi.org/10.1007/3-540-48159-1_16CrossRefGoogle Scholar
  3. 3.
    Amgoud, L., Kaci, S.: An argumentation framework for merging conflicting knowledge bases. Int. J. Approximate Reasoning 45(2), 321–340 (2007). An argumentation framework for merging conflicting knowledge basesMathSciNetCrossRefGoogle Scholar
  4. 4.
    Benferhat, S., Dubois, D., Kaci, S., Prade, H.: Possibilistic merging and distance-based fusion of propositional information. Ann. Math. Artif. Intell. 34(1–3), 217–252 (2002)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Nguyen, T.H.K., Tran, T.H., Nguyen, T.V., Le, T.T.L.: Merging Possibilistic Belief Bases by Argumentation. In: Nguyen, N.T., Tojo, S., Nguyen, L.M., Trawiński, B. (eds.) ACIIDS 2017. LNCS (LNAI), vol. 10191, pp. 24–34. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-54472-4_3CrossRefGoogle Scholar
  6. 6.
    Qi, G., Du, J., Liu, W., Bell, D.A.: Merging knowledge bases in possibilistic logic by lexicographic aggregation. In: Grünwald, P., Spirtes, P. (eds.) Proceedings of the Twenty-Sixth Conference on Uncertainty in Artificial Intelligence, UAI 2010, Catalina Island, CA, USA, 8–11 July 2010, pp. 458–465. AUAI Press (2010)Google Scholar
  7. 7.
    Benferhat, S., Kaci, S.: Fusion of possibilistic knowledge bases from a postulate point of view. Int. J. Approximate Reasoning 33, 255–285 (2003)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Gabbay, D., Rodrigues, O.: A numerical approach to the merging of argumentation networks. In: Fisher, M., van der Torre, L., Dastani, M., Governatori, G. (eds.) CLIMA XIII 2012. LNCS, vol. 7486, pp. 195–212. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-32897-8_14CrossRefGoogle Scholar
  9. 9.
    Tran, T.H., Nguyen, N.T., Vo, Q.B.: Axiomatic characterization of belief merging by negotiation. Multimed. Tools Appl. 1–27 (2012)Google Scholar
  10. 10.
    Tran, T.H., Vo, Q.B.: An axiomatic model for merging stratified belief bases by negotiation. In: Nguyen, N.-T., Hoang, K., Jȩdrzejowicz, P. (eds.) ICCCI 2012. LNCS (LNAI), vol. 7653, pp. 174–184. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-34630-9_18CrossRefGoogle Scholar
  11. 11.
    Qi, G., Liu, W., Bell, D.A.: Combining multiple prioritized knowledge bases by negotiation. Fuzzy Sets Syst. 158(23), 2535–2551 (2007)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Booth, R.: A negotiation-style framework for non-prioritised revision. In: Proceedings of the 8th Conference on Theoretical Aspects of Rationality and Knowledge, TARK 2001, pp. 137–150. Morgan Kaufmann Publishers Inc. (2001)Google Scholar
  13. 13.
    Booth, R.: Social contraction and belief negotiation. Inf. Fusion 7, 19–34 (2006)CrossRefGoogle Scholar
  14. 14.
    Dubois, D., Prade, H.: Possibilistic logic: an overview. In: Gabbay, D.M., Siekmann, J., Woods, J. (eds.) Computational Logic, Volume 9 of Handbook of the History of Logic, pp. 197–255. Elsevier (2014)Google Scholar
  15. 15.
    Yager, R.R., Liu, L.P. (eds.): Classic Works of the Dempster-Shafer Theory of Belief Functions. Springer, Heidelberg (2008).  https://doi.org/10.1007/978-3-540-44792-4CrossRefzbMATHGoogle Scholar
  16. 16.
    Dubois, D.: Belief structures, possibility theory and decomposable measures on finite sets. Comput. AI 5, 403–416 (1986)zbMATHGoogle Scholar
  17. 17.
    Walley, P.: Statistical Reasoning with Imprecise Probabilities. Chapman and Hall, London (1991)CrossRefGoogle Scholar
  18. 18.
    Zhang, D.: A logic-based axiomatic model of bargaining. Artif. Intell. 174, 1307–1322 (2010)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Tran, T.H., Vo, Q.B., Kowalczyk, R.: Merging belief bases by negotiation. In: König, A., Dengel, A., Hinkelmann, K., Kise, K., Howlett, R.J., Jain, L.C. (eds.) KES 2011. LNCS (LNAI), vol. 6881, pp. 200–209. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-23851-2_21CrossRefGoogle Scholar
  20. 20.
    Tran, T.H., Vo, Q.B., Nguyen, T.H.K.: On the belief merging by negotiation. In: 18th International Conference in Knowledge Based and Intelligent Information and EngineeringGoogle Scholar
  21. 21.
    Tran, T.H., Nguyen, T.H.K., Ha, Q.T., Vu, N.T.: Argumentation framework for merging stratified belief bases. In: Nguyen, N.T., Trawiński, B., Fujita, H., Hong, T.-P. (eds.) ACIIDS 2016. LNCS (LNAI), vol. 9621, pp. 43–53. Springer, Heidelberg (2016).  https://doi.org/10.1007/978-3-662-49381-6_5CrossRefGoogle Scholar
  22. 22.
    Deng, Y., OuYang, Y.: A belief revision method based on argumentative dialogue model. In: The 11th International Conference on Computer Science & Education, ICCSE 2016, Nagoya University, Japan, 23–25 August (2016)Google Scholar
  23. 23.
    Benferhat, S., Benferhat, J., Hué, J., Lagrue, S., Rossit, J.: Interval-based possibilistic logic. In: Walsh, T. (ed.) Proceedings of 22nd International Joint Conference on Artificial Intelligence (IJCAI 2011), Barcelona, 16–22 July, pp. 750–755 (2011)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.University of Engineering and Technology, Vietnam National UniversityHanoiVietnam
  2. 2.University of Finance and AccountancyTu NghiaVietnam

Personalised recommendations