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A study of explanation generation in a rule-based system

Abstract : The concept of “Business Rule Management System” (BRMS) has been introduced in order to facilitate the design, the management and the execution of company-specific business policies. Based on a symbolic approach, the main idea behind these tools is to enable the business users to manage the business rule changes in the system without requiring programming skills. It is therefore a question of providing them with tools that enable to formulate their business policies in a near natural language form and automate their processing. Nowadays, with the expansion of intelligent systems, we have to cope with more and more complex decision logic and large volumes of data. It is not straightforward to identify the causes leading to a decision. There is a growing need to justify and optimize automated decisions in a short time frame, which motivates the integration of advanced explanatory component into its systems. Thus, the main challenge of this research is to provide an industrializable approach for explaining the decision-making processes of business rules applications and more broadly rule-based systems. This approach should be able to provide the necessary information for enabling a general understanding of the decision, to serve as a justification for internal and external entities as well as to enable the improvement of existing rule engines. To this end, the focus will be on the generation of the explanations in themselves as well as on the manner and the form in which they will be delivered.
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Submitted on : Thursday, March 8, 2018 - 10:15:14 AM
Last modification on : Sunday, October 25, 2020 - 2:46:17 PM
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  • HAL Id : tel-01726252, version 1


Karim El Mernissi. A study of explanation generation in a rule-based system. Artificial Intelligence [cs.AI]. Université Pierre et Marie Curie - Paris VI, 2017. English. ⟨NNT : 2017PA066332⟩. ⟨tel-01726252⟩



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