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AI-Enhanced Hybrid Decision Management

dc.contributor.authorBork, Dominik
dc.contributor.authorAli, Syed Juned
dc.contributor.authorDinev, Georgi Milenov
dc.date.accessioned2023-04-19T04:38:17Z
dc.date.available2023-04-19T04:38:17Z
dc.date.issued2023
dc.description.abstractThe Decision Model and Notation (DMN) modeling language allows the precise specification of business decisions and business rules. DMN is readily understandable by business users involved in decision management. However, as the models get complex, the cognitive abilities of humans threaten manual maintainability and comprehensibility. Proper design of the decision logic thus requires comprehensive automated analysis of e.g., all possible cases the decision shall cover; correlations between inputs and outputs; and the importance of inputs for deriving the output. In the paper, the authors explore the mutual benefits of combining human-driven DMN decision modeling with the computational power of Artificial Intelligence for DMN model analysis and improved comprehension. The authors propose a model-driven approach that uses DMN models to generate Machine Learning (ML) training data and show, how the trained ML models can inform human decision modelers by means of superimposing the feature importance within the original DMN models. An evaluation with multiple real DMN models from an insurance company evaluates the feasibility and the utility of the approach.de
dc.identifier.doi10.1007/s12599-023-00790-2
dc.identifier.pissn1867-0202
dc.identifier.urihttp://dx.doi.org/10.1007/s12599-023-00790-2
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/41221
dc.publisherSpringer
dc.relation.ispartofBusiness & Information Systems Engineering: Vol. 65, No. 2
dc.relation.ispartofseriesBusiness & Information Systems Engineering
dc.subjectArtificial intelligence
dc.subjectDMN
dc.subjectEnterprise modeling
dc.subjectExplainable AI
dc.subjectMachine learning
dc.subjectModel-driven engineering
dc.titleAI-Enhanced Hybrid Decision Managementde
dc.typeText/Journal Article
gi.citation.endPage199
gi.citation.startPage179

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