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Communication Dans Un Congrès Année : 2014

Uncertain Reasoning for Business Rules

Hamza Agli
Philippe Bonnard
  • Fonction : Auteur
Christophe Gonzales
Pierre-Henri Wuillemin

Résumé

Business rules (BRs) have been widely adopted within decision making processes in the industrial fields (banking, assurance, transport ...). A BR is a high level description allowing non-computer scientists to author and/or make a decision by the use of vocabulary and concepts specific to the organization. It encompasses the business knowledge of experts and separates clearly the business logic from the application logic which implements it by defining and authoring it through a very structured and connected set of applications called a Business Rule Management System (BRMS). In this paper we propose to investigate the possibility of integration of probabilistic reasoning in a business rules-based system. As a consequence, we can deal with incoherent and incomplete data. Our approach is to extend an object BR model with a probabilistic model. This will be done by coupling business rules and probabilistic engines . The result will allow to perform inferences in Bayesian networks and Probabilistic Relation Models (PRMs) in order to sophisticate the calculations performed in classical BR inference.
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Dates et versions

hal-01215676 , version 1 (14-10-2015)

Identifiants

  • HAL Id : hal-01215676 , version 1

Citer

Hamza Agli, Philippe Bonnard, Christophe Gonzales, Pierre-Henri Wuillemin. Uncertain Reasoning for Business Rules. RuleML doctoral consortium, Aug 2014, Prague, Czech Republic. ⟨hal-01215676⟩
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