Le concept de “Business Rule Management System” (BRMS) a été introduit pour faciliter la création, la vérification, le déploiement et l'exécution des politiques commerciales propres à chaque compagnie. Basée sur une approche symbolique, l'idée générale est de permettre aux utilisateurs métier de gérer les changements des règles métier dans le système sans avoir besoin de recourir à des compétences techniques. Il s'agit donc de fournir à ces derniers la possibilité de formuler des politiques commerciales et d'automatiser leur traitement tout en restant proche du langage naturel. De nos jours, avec l'expansion de ce type de systèmes, il faut faire face à des logiques de décision de plus en plus complexes et à de larges volumes de données. Il n'est pas toujours facile d'identifier les causes conduisant à une décision. On constate ainsi un besoin grandissant de justifier et d'optimiser les décisions dans de courts délais qui induit l'intégration à ses systèmes d'une composante d'explication évoluée. Le principal enjeu de ces recherches est de fournir une approche industrialisable de l'explication des processus de décision d'un BRMS et plus largement d'un système à base de règles. Cette approche devra être en mesure d'apporter les informations nécessaires à la compréhension générale de la décision, de faire office de justification auprès d'entités internes et externes ainsi que de permettre l'amélioration des moteurs de règles existants. La réflexion se portera tant sur la génération des explications en elles-mêmes que sur la manière et la forme sous lesquelles elles seront délivrées. / 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.

Disertacijoje tiriamas sprendimų modelių kūrimas verslo taisyklių ir procesų modelių pagalba bei šių modelių transformacijos į programų sistemų komponentus realizuojančius automatizuotus sprendimus informacinėje sistemoje. Analitinėje disertacijos dalyje apžvelgiami sprendimu modeliai ir metodai skirti intelektualizuotų informacinių sistemų kūrimui. Tolimesniuose skyriuose pateikiamas, remiantis analizės rezultatais, sukurtas modelių karkasas ir jo pagrindu sukurtas metodas skirtas sprendimų modelių automatizavimui. Baigiamajame disertacijos skyriuje aprašomi eksperimentai, kuriuose atliekamos sprendimų modelių transformacijos. Galiausiai pateikiami eksperimentų rezultatai ir galutinės išvados. / In the presented thesis we offer modernisation of information system development methods used for implementation of automated information-, rule-, knowledge- and model-based decision processes assisted by early separation and development of a business logic model and implementation of decision-making and knowledge discovery process models with further support to business people with suitable interfaces for modification of decision-making processes without involvement of software developers in later exploitation stages. In the analytical part of the dissertation, decision models and methods of intellectualised information systems are analysed. According to the results obtained during the analysis, investigation on the proposed framework and a decision model based method for decision-making process automation is carried out. In the final chapter several experiments are described in order to evaluate the proposed method, and the general conclusions complete the research.

This thesis develops a new abstraction for solving problems in decision automation. Decision automation is the process of creating algorithms which use data to make decisions without the need for human intervention. In this abstraction, four key ideas/problems are highlighted which must be considered when solving any decision problem. These four problems are the decision problem, the learning problem, the model reduction problem, and the verification problem. One of the benefits of this abstraction is that a wide range of decision problems from many different areas can be broken down into these four “key” sub-problems. By focusing on these key sub-problems and the interactions between them, one can systematically arrive at a solution to the original problem. Three new learning platforms have been developed in the areas of portfolio optimization, business intelligence, and automated water management in order to demonstrate how this abstraction can be applied to three different types of problems. For the automated water management platform a full solution to the problem is developed using this abstraction. This yields an automated decision process which decides how much water to release from the Piute Reservoir into the Sevier River during an irrigation season. Another motivation for developing these learning platforms is that they can be used to introduce students of all disciplines to automated decision making.