Autor: Stefan Bosse

Designing and simulating complex mechatronic and distributed intelligent systems requires a unified system modeling and programming language. This paper presents JavaScript as a unified modeling and programming language by extending JavaScript with a semantic type system JST to bridge the gap between models and implementations. The result is the JS+ Superset language, which combines typing, modeling and programming. Different model domains and their relation to the JS+ programming model including some generic transformation rules are shown using the example of a sensory material. Finally, the multidomain simulation tool SEJAM is introduced, which combines physical and data processing simulation with agents.

Load bearing structures are typically designed with respect to relevant load cases, assuming static shapes and given material properties that are selected during design and material selection. An example of such a material is a particular class of polymers that are capable of changing their elasticity based on the influence of optical, thermal or electrical fields. One problem to be solved in terms of active intelligent cellular structures is the correlated and self-organizing control of cell response and control and the underlying information organization that must provide robustness and real-time capabilities. We propose a hybrid approach combining mobile and reactive self-organizing multi-agent systems (MAS) and machine learning. The MAS is the essential robust information communication technology (ICT). The agents are executed in material-integrated networks consisting of microcomputers. The simulation and implementation of such complex systems is a major challenge.

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 ...

Today sensors and actuators gain increased importance in industrial production processes. Traditionally centralized processing architectures are used for sensor data processing and actuator control. Increasing sensor and actuator densities require new decentralized and distributed processing methods and architectures. Artificial Intelligence, part of computer science, can contribute to robustness and autonomy aspects concerning data processing and distribution.