Supervision : Christophe GONZALES

Co-supervision : WUILLEMIN Pierre-Henri, BONNARD Philippe, DE SAINTE MARIE Christian

Uncertain Reasoning for Business Rules

In this thesis, we address the issue of uncertainty in Object-Oriented Business Rules Management Systems (OO-BRMSs). To achieve this aim, we rely on Probabilistic Relational Models (PRMs). These are an object-oriented extension of Bayesian Networks that can be exploited to efficiently model probability distributions in OO-BRMSs.
It turns out that queries in OO-BRMS are numerous and we need to request the PRM very frequently. The PRM should then provide a rapid answer. For this reason, we propose, in the first part of this thesis, a new algorithm that respects two specificities of OO-BRMSs and optimizes the probabilistic inference accordingly. First, OO-BRMSs queries affect only a subset of their base, hence, the probabilities of interest concern only a subset of the PRM random variables. Second, successive requests differ only slightly from each other. We prove theoretically the correctness of the proposed algorithm and we highlight its efficiency through experimental tests.
During the second part, we establish general principles for probabilistic OO-BRMSs and we describe an approach to couple them with PRMs. Then, we apply the approach to IBM Operational Decision Manager (ODM), one of the state-of-the-art OO-BRMSs, and we provide an overview of the resulted prototype.
Finally, we discuss advanced techniques to compile elements of ODM technical language into instructions that are exploitable by the PRM probabilistic engine and we provide some directions for future works.

2014-2018 Publications

• 2018
  • H. Agli, Ph. Bonnard, Ch. Gonzales, P.‑H. Wuillemin : “Inférence incrémentale pour les modèles probabilistes relationnels et application aux systèmes à base de règles orientés objet”, Revue des Sciences et Technologies de l'Information - Série RIA : Revue d'Intelligence Artificielle, vol. 32 (1), Réseaux bayésiens et modèles probabilistes, pp. 111-132, (Lavoisier) (2018)
• 2017
  • H. Agli : “Raisonnement incertain pour les règles métiers”, thesis, phd defence 07/20/2017, supervision Gonzales, Christophe, co-supervision : Wuillemin, Pierre-Henri, Bonnard, Philippe, De, SAINTE MARIE Christian (2017)
• 2016
  • H. Agli, Ph. Bonnard, Ch. Gonzales, P.‑H. Wuillemin : “Un algorithme d’arbre de jonction incrémental”, 8^es journées francophones de réseaux bayésiens (JFRB 2016), Clermont-Ferrand, France (2016)
  • H. Agli, Ph. Bonnard, Ch. Gonzales, P.‑H. Wuillemin : “Business Rules Uncertainty Management with Probabilistic Relational Models”, RuleML16, Stony Brook, New York, United States (2016)
  • H. Agli, Ph. Bonnard, Ch. Gonzales, P.‑H. Wuillemin : “Incremental Junction Tree Inference”, IPMU16, Eindhoven, Netherlands (2016)
• 2014
  • H. Agli, Ph. Bonnard, Ch. Gonzales, P.‑H. Wuillemin : “Uncertain Reasoning for Business Rules”, RuleML doctoral consortium, vol. 1211, CEUR Workshop Proceedings, Prague, Czechia, (CEUR-WS.org) (2014)