A. M. Wilbik

We live in a hyper-connected world, where many systems are connecting to each other and influencing each other. This complex network of systems can produce large amounts of data. Both the network and the nature of the data it produces can evolve fast. Decisions need to be taken on a continuous basis in those complex settings addressing multiple criteria. People alone cannot deal with the amount and complexity of data, and machines alone are missing the required problem domain overview and context knowledge. Therefore, there is need for intelligent interaction between the human and the machine in data-driven decision making.

Decisions are in the centre of intelligent interaction. Those decisions are diffused into actions that constitute processes, which are executed in a business or societal context. The data generated by those processes can be stored in many information systems and are of a heterogenous nature. Data coming from multiple systems and other sources including IoT and social media, are transformed into information using data fusion and data science methods. This information combined with the tacit knowledge from the user is the basis for decisions. The context provides requirements for those decisions, i.e. the criteria to take into account in a decision. This closes the loop of the multi-criteria data-driven decision making in a complex system, which is the essence of Intelligent Interaction. 

My research focus is on building three interrelated research lines:

• Supporting interaction between machine and human for joint decision making: There is a need for an intelligent interaction between human and machine supporting joint decision making, e.g., by allowing a machine to explain a decision but also incorporating people’s arguments and reasoning in the decision model. Hence this would enable having a ‘discussion’ with the machine for the best decision making.
• Information and data fusion: With the data stored in multiple sources, there is a need for techniques and methods integrating those heterogeneous data as well as analysing data with complex and non-standard structures.
• Decision contextualizing: In the Intelligent Interaction framework decisions are diffused and influence multiple systems, which can have different objectives. Therefore, in reaching the right decision those objectives should be considered, hence requiring a multi-criteria decision making. However, the challenge is that in many applications the data dimensions do not match the decision criteria.

• Pietrasik, M., Wilbik, A., & Grefen, P. W. P. J. (2024). The enabling technologies for digitalization in the chemical process industry. Digital Chemical Engineering, 12, Article 100161. https://doi.org/10.1016/j.dche.2024.100161

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• Yurt, Z. O., Eshuis, R., Wilbik, A., & Vanderfeesten, I. (2025). Guidance for goal achievement in knowledge-intensive processes using intuitionistic fuzzy sets. Expert Systems with Applications, 260, Article 125417. https://doi.org/10.1016/j.eswa.2024.125417

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• Grefen, P., Vanderfeesten, I., Wilbik, A., Comuzzi, M., Ludwig, H., Serral, E., Kuitems, F., Blanken, M., & Pietrasik, M. (2023). Towards Customer Outcome Management in Smart Manufacturing. Machines, 11(6), Article 636. https://doi.org/10.3390/machines11060636

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• Khan, A., ten Thij, M., & Wilbik, A. (2022). Communication-Efficient Vertical Federated Learning. Algorithms, 15(8), Article 273. https://doi.org/10.3390/a15080273

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• Stallmann, M., & Wilbik, A. (2022). On a Framework for Federated Cluster Analysis. Applied Sciences, 12(20), Article 10455. https://doi.org/10.3390/app122010455

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• Gilsing, R., Wilbik, A., Grefen, P., Turetken, O., Ozkan, B., Adali, O. E., & Berkers, F. (2021). Defining business model key performance indicators using intentional linguistic summaries. Software and Systems Modeling, 20(4), 965-996. https://doi.org/10.1007/s10270-021-00894-x

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• van Loon, S. L. M., Deneer, R., Nienhuijs, S. W., Wilbik, A., Kaymak, U., van Riel, N., Scharnhorst, V., & Boer, A.-K. (2020). Metabolic Health Index (MHI): Assessment of Comorbidity in Bariatric Patients Based on Biomarkers. Obesity Surgery, 30(2), 714-724. https://doi.org/10.1007/s11695-019-04244-1

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• Dijkman, R. M., & Wilbik, A. (2017). Linguistic summarization of event logs - A practical approach. Information Systems, 67, 114-125. https://doi.org/10.1016/J.IS.2017.03.009

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• Wilbik, A., & Keller, J. M. (2012). A distance metric for a space of linguistic summaries. Fuzzy Sets and Systems, 208, 79-94. https://doi.org/10.1016/J.FSS.2012.03.010

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• Kacprzyk, J., Wilbik, A., & Zadrozny, S. (2008). Linguistic summarization of time series using a fuzzy quantifier driven aggregation. Fuzzy Sets and Systems, 159(12), 1485-1499. https://doi.org/10.1016/J.FSS.2008.01.025

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https://cris.maastrichtuniversity.nl/en/persons/anna-wilbik