Abstract: To generate a decision logic for a controller of an Industrial Automation Control System, IACS, an iterative process is performed in which a decision logic candidate for the decision logic is generated, and a performance of the decision logic candidate in response to scenarios is computed.

Abstract: Performance of a decision-making logic (35) of a controller (31) of an industrial automation control system is monitored during field operation of the controller (31). A supervisory system (20) receives operational data collected during field operation of the controller (31). The supervisory system performs an analysis of the operational data to assess performance of the decision-making logic (35), using pre-operational data generated prior to field operation of the controller (31) and/or a performance assessment logic (27) generated prior to field operation of the controller (31).

Abstract: Machine learning, ML, using labeled data is performed to thereby train a ML model to generate decision logic for a controller of an IACS, in particular a power system controller. The ML model has ML model inputs and ML model outputs. The ML model is decomposed into a plurality of computational sub-processes, wherein intermediate signals output by one of the computational sub-processes are input into a consecutive one of the computational sub-processes. Information on the trained ML model is generated based on the intermediate signals and is output for interpretation, verification, visualization, and/or inspection of at least one of the computational sub-processes.