Business Rules Uncertainty Management with Probabilistic Relational Models

被引:3
作者
Agli, Hamza [1 ]
Bonnard, Philippe [1 ]
Gonzales, Christophe [2 ]
Wuillemin, Pierre-Henri [2 ]
机构
[1] IBM France Lab, Gentilly, France
[2] UPMC Univ Paris 6, Sorbonne Univ, CNRS, UMR LIP6 7606, Paris, France
来源
RULE TECHNOLOGIES: RESEARCH, TOOLS, AND APPLICATIONS | 2016年 / 9718卷
关键词
Business rules management systems; Uncertainty management; Probabilistic Relational Models; Bayesian Networks; INFERENCE; SYSTEMS; PATTERN;
D O I
10.1007/978-3-319-42019-6_4
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
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.
引用
收藏
页码:53 / 67
页数:15
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