Modeling of Production Processes with Mathematical LogicŠircelj, Beno (Avtor) Moškon, Miha (Mentor) Košmerlj, Aljaž (Komentor) modelingmachine learningsmart factoriesprobabilistic soft logiclogic networksIn this work, we show how Probabilistic Soft Logic (PSL) can be used to build production forecasting models, which to the best of our knowledge has not been done before. This type of forecasting is trying to predict the state of the processes in dependence of the current state of the system, such as any failures or yields. The models are created and analyzed based on a real use case in the petroleum industry. The models using PSL represent the characteristics of the underlying processes by assigning the appropriate weights to a set of PSL rules. We show how such a rule set can be constructed and how the weights can be set using four different weight learning methods. For comparison, we build three standard machine learning models. In our experiments, the models built with PSL are inferior to the other methods in terms of accuracy and computation time, making them inefficient in their current form. We discuss the possibilities to increase the applicability of the proposed implementation to the production forecasting using digital twins.20212021-06-30 09:30:00Magistrsko delo/naloga127972VisID: 29081COBISS_ID: 69237507sl