Industrial Agents and Agent-Based Learning in a Technical Context

Today data processing becomes more and more complex concerning the amount of data to be processed, the data dimension and correlation and the relationship between derived information and inputdata. This is the case especially in sensing and measuring processes. Measuring uncertainties, calibration errors, and unreliability of sensors have a significant impact on the derivation quality of suitable information. In the technical and industrial context the raising complexity and distribution of data processing is a special issue. Commonly, information is derived from raw input data by using some kind of mathematical model and functions, but often being incomplete. If reasoning of system states is primarily desired, Machine Learning can be an alternative. Tradionally, sensor data is acquired and delivered to and processed by a central processing unit. In this paper, the deployment of distributed Machine Learning using mobile Agents forming self-organizing systems is discussed and posing the benefit for the enhancement of the sensor and data processing in technical and industrial systems. This also addresses the quality of the computed results, the efficiency and the reliability.