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Recent advancements in predictive machine learning has led to its applicationin various use cases in manufacturing. Most research focused on maximisingpredictive accuracy without addressing the uncertainty associated with it.While accuracy is important, focusing primarily on it poses an overfittingdanger, exposing manufacturers to risk, ultimately hindering the adoption ofthese techniques. In this paper, we determine the sources of uncertainty inmachine learning and establish the success criteria of a machine learningsystem to function well under uncertainty in a cyber-physical manufacturingsystem (CPMS) scenario. Then, we propose a multi-agent system architecturewhich leverages probabilistic machine learning as a means of achieving suchcriteria. We propose possible scenarios for which our proposed architecture isuseful and discuss future work. Experimentally, we implement Bayesian NeuralNetworks for multi-tasks classification on a public dataset for the real-timecondition monitoring of a hydraulic system and demonstrate the usefulness ofthe system by evaluating the probability of a prediction being accurate givenits uncertainty. We deploy these models using our proposed agent-basedframework and integrate web visualisation to demonstrate its real-timefeasibility.

Multi Agent System for Machine Learning Under Uncertainty in Cyber Physical Manufacturing System