multi-agent-systems

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 ...

This paper describes an autonomic machine control system applied to the adaptive control of a modular soldering machine. It covers customer requirements, design principles as well as benefits and advantages by making use of software agent technology. The particular case concerns the creation of a novel modular production machine currently running as a pilot, which will be sold worldwide from mid of the year.

Visteon is one of the largest automotive suppliers world-wide and is focused on innovation and permanent improvement. For Visteon that means the consistent implementation of Lean Manufacturing concepts. Thereby lean in-house logistics represent a core aspect. For that reason the Visteon plant at Düren is developing innovative concepts for the dynamic controlling of the overall material flow together with the chair of production systematics of the university of Aachen. With the implementation of the simple yet flexible variant Kanban system Visteon plant Düren is obtaining a leading position with regard to innovative in-house logistics.

Flexibility and transformability of modern manufacturing are determined by many different factors. One of them is an adaptable and dynamic planning of manufacturing processes. The planning systems have not only to guarantee a stable, reliable and quick planning, but also to react to changes and disturbances flexibly and adeqautely. One promising concept is the multi-agents technology. In this work mechanisms and methods for such an agent-based planning are presented that can be applied to the turbulent manufacturing environment.