Business Rules Uncertainty Management with Probabilistic Relational Models

  • Hamza Agli
  • Philippe Bonnard
  • Christophe Gonzales
  • Pierre-Henri Wuillemin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9718)


Object-oriented Business Rules Management Systems (OO-BRMS) are a complex applications platform that provide tools for automating day-to-day business decisions. To allow more sophisticated and realistic decision-making, these tools must enable Business Rules (BRs) to handle uncertainties in the domain. For this purpose, several approaches have been proposed, but most of them rely on heuristic models that unfortunately have shortcomings and limitations. In this paper we present a solution allowing modern OO-BRMS to effectively integrate probabilistic reasoning for uncertainty management. This solution has a coupling approach with Probabilistic Relational Models (PRMs) and facilitates the inter-operability, hence, the separation between business and probabilistic logic. We apply our approach to an existing BRMS and discuss implications of the knowledge base dynamicity on the probabilistic inference.


Business rules management systems Uncertainty management Probabilistic Relational Models Bayesian Networks 



This work was partially supported by IBM France Lab/ANRT CIFRE under the grant #421/2014. The authors would like to thank Christian De Sainte Marie for useful discussions and insights.


  1. 1.
    Agli, H., Bonnard, P., Wuillemin, P., Gonzales, C.: Uncertain reasoning for business rules. In: Proceedings of the 8th International Web Rule Symposium Doctoral Consortium (2014)Google Scholar
  2. 2.
    Agli, H., Bonnard, P., Wuillemin, P., Gonzales, C.: Incremental junction tree inference. In: Proceedings of the 16th Information Processing and Management of Uncertainty in Knowledge-Based Systems International Conference (2016, to appear)Google Scholar
  3. 3.
    Berstel-Da Silva, B.: Verification of Business Rules Programs. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  4. 4.
    Bobek, S., Nalepa, G.J.: Compact representation of conditional probability for rule-based mobile context-aware systems. In: Bassiliades, N., Gottlob, G., Sadri, F., Paschke, A., Roman, D. (eds.) RuleML 2015. LNCS, vol. 9202, pp. 83–96. Springer, Heidelberg (2015)CrossRefGoogle Scholar
  5. 5.
    Buchanan, B.G., Shortliffe, E.H.: Rule Based Expert Systems: The Mycin Experiments of the Stanford Heuristic Programming Project. Addison-Wesley, Reading (1984)Google Scholar
  6. 6.
    De Raedt, L., Kimmig, A.: Probabilistic (logic) programming concepts. Mach. Learn. 100(1), 5–47 (2015)MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    Elkan, C.: The paradoxical success of fuzzy logic. In: IEEE Expert, pp. 698–703 (1993)Google Scholar
  8. 8.
    Forgy, C.L.: RETE: a fast algorithm for the many pattern/many object pattern match problem. Artif. Intell. 19(1), 17–37 (1982)CrossRefGoogle Scholar
  9. 9.
    Graham, I.: Business Rules Management and Service Oriented Architecture: A Pattern Language. Wiley, Chichester (2006)Google Scholar
  10. 10.
    Hart, P.E., Duda, R.O., Einaudi, M.T.: PROSPECTOR–a computer-based consultation system for mineral exploration. J. Int. Assoc. Math. Geol. 10(5), 589–610 (1977)CrossRefGoogle Scholar
  11. 11.
    Heckerman, D.E., Shortliffe, E.H.: From certainty factors to belief networks. Artif. Intell. Med. 4(1), 35–52 (1992)CrossRefGoogle Scholar
  12. 12.
    Koller, D., Pfeffer, A.: Probabilistic frame-based systems. In: Proceedings of the 15th National Conference on Artificial Intelligence (AAAI), pp. 580–587 (1998)Google Scholar
  13. 13.
    Koller, D., Pfeffer, A.: Object-oriented Bayesian networks. In: Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence (UAI), pp. 302–313 (1997)Google Scholar
  14. 14.
    Korver, M., Lucas, P.J.F.: Converting a rule-based expert system into a belief network. Med. Informatics 18, 219–241 (1993)CrossRefGoogle Scholar
  15. 15.
    Mahoney, S.M., Laskey, K.B.: Network engineering for complex belief networks. In: Proceedings of the Twelfth International Conference on Uncertainty in Artificial Intelligence (UAI), pp. 389–396 (1996)Google Scholar
  16. 16.
    Ng, K.C., Abramson, B.: Uncertainty management in expert systems. IEEE Expert Intell. Syst. Appl. 5(2), 29–48 (1990)Google Scholar
  17. 17.
    Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Mateo (1988)zbMATHGoogle Scholar
  18. 18.
    Pfeffer, A.J.: Probabilistic Reasoning for Complex Systems. Ph.D. thesis, Stanford University (2000)Google Scholar
  19. 19.
    Torti, L., Gonzales, C., Wuillemin, P.H.: Speeding-up structured probabilistic inference using pattern mining. Int. J. Approximate Reasoning 54(7), 900–918 (2013)MathSciNetCrossRefzbMATHGoogle Scholar
  20. 20.
    Wuillemin, P.H., Torti, L.: Structured probabilistic inference. Int. J. Approximate Reasoning 53(7), 946–968 (2012)MathSciNetCrossRefzbMATHGoogle Scholar
  21. 21.
    Zadeh, L.A.: Fuzzy sets. Inform. Control 8(3), 338–353 (1965)MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Hamza Agli
    • 1
  • Philippe Bonnard
    • 1
  • Christophe Gonzales
    • 2
  • Pierre-Henri Wuillemin
    • 2
  1. 1.IBM France LabGentillyFrance
  2. 2.Sorbonne Universités, UPMC Univ Paris 6, CNRS, UMR 7606 LIP6ParisFrance

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