Abstract
The paper presents an attempt to conceptualize decision support and various generic subtasks, to develop a general architecture of intelligent decision support systems, and to exploit previous work on process-oriented diagnosis within this architecture. The primary subtasks whose (intelligent) solution is heavily dependent on domain knowledge are situation assessment, i.e. inferring what is happening in a system from a set of observations, and therapy proposal, i.e. developing plans for interventions to achieve certain goals starting from the current situation. Both tasks can be solved by an extension of consistency-based diagnosis to process-oriented models.
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Acknowledgements
Many thanks to Ulrich Heller for contributing to the foundations of this work and to Hosain Ibna Bashar, Iliya Valchev, and Xin Ruan for discussing and implementing some of the ideas, our former Brazilian project partners, Elenara Lersch, Waldir Roque, Paulo Salles, as well as Radhika Selvamani and Udai Agarwal from VIT Chennai for helping to improve the paper.
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Struss, P. (2020). Model-Based Decision Support Systems - Conceptualization and General Architecture. In: Fujita, H., Fournier-Viger, P., Ali, M., Sasaki, J. (eds) Trends in Artificial Intelligence Theory and Applications. Artificial Intelligence Practices. IEA/AIE 2020. Lecture Notes in Computer Science(), vol 12144. Springer, Cham. https://doi.org/10.1007/978-3-030-55789-8_51
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DOI: https://doi.org/10.1007/978-3-030-55789-8_51
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