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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)

Abstract

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.

Keywords

Business rules management systems Uncertainty management Probabilistic Relational Models Bayesian Networks 

Notes

Acknowledgments

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.

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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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