Home » ECSA 2016 » Section S2: Smart Systems and Structures » Paper S2004

[S2004] Industrial Agents and Distributed Agent-based Learning

University of Bremen, Dept. of Mathematics and Computer Science, Robert Hooke Str. 5, 28359 Bremen, Germany
15 November 2016
5/5 rated ( 1 ratings )


Today sensor data processing and information mining become more and more complex concerning the amount of sensor data to be processed, the data dimension, the data quality, and the relationship between derived information and input data. This is the case especially in large-scale sensing and measuring processes embedded in Cloud environments. 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 or unknown. If reasoning of statements is primarily desired, Machine Learning can be an alternative. Traditionally, 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 and self-adaptive systems (self-X) 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 statements, e.g., an accurate  prediction  of run-time parameters like  mechanical loads or health conditions, the efficiency, and the reliability in the presence of partial system failures.


Mobile Agents, Self-organizing Systems, Distributed Machine Learning, Sensor Clouds

Cite this article as

Bosse, S. Industrial Agents and Distributed Agent-based Learning. In Proceedings of the 3rd Int. Electron. Conf. Sens. Appl., 15–30 November 2016; Sciforum Electronic Conference Series, Vol. 3, 2016 , S2004; doi:10.3390/ecsa-3-S2004


Author biographies

Stefan Bosse
Stefan Bosse studied physics at the University of Bremen. He received a Doctoral Degree (Dr. rer. nat.) in physics in the year 2002 at the University of Bremen. In the year 2004 he joined the Department of Mathematics & Computer Science and the working group robotics. He works as a senior researcher and lecturer. Since 2002 his scientific work focuses on Parallel and Distributed Systems, data processing in large-scale Sensor Networks and the Internet with Multi-Agent Systems, Material-Integrated Sensing Systems and Material Informatics, System-on-Chip design and Synthesis, Compiler Construction, and various topics in Artificial Intelligence including Machine Learning and Self-organizing Systems. He teaches several courses at the University of Bremen in fundamental computer science and in selected advanced topics covering the design of Embedded data processing Systems, Parallel and Multi-agent System design, High-level Synthesis, and Material-integrated Sensing Systems with a high interdisciplinary background. Since 2008 he is engaged in the ISIS Sensorial Materials Scientific Centre of the University of Bremen pushing interdisciplinary research and filling the gap between Technology and Computer Sciences and establishing Material Informatics, i.e., new advanced computing embedded in materials and technical structures.

Comments on Industrial Agents and Distributed Agent-based Learning