Autonomieerhöhung durch Lernen. Teil II: Erlernen von Heuristiken, Planungs- und Kommunikationsgrundlagen

Für die Kooperation von Mensch und Roboter - z.B. im normalen Haushalt - sind menschenähnliche Dienstleistungsroboter von Interesse. Für ihre Steuerung sind Autonomie und Selbstadaption durch Lernen notwendig. Die Arbeit behandelt ausgehend von einer dafür geeigneten Architektur Lernvorgänge auf den oberen, für selbständiges Handeln zuständigen Ebenen. Grundsätzliches und Lernen auf den unteren, motorischen Ebenen wurden in einer vorausgegangenen Arbeit erörtert

A New Concept for Learning Control Inspired by Brain Theory

The paper explains an unconventional learning control method based on assumptions in the literature about human problem solving and information storage in neuronal networks. The on-line learning comprises two stages: The dynamic input-output behaviour of the process to be controlled is stored stepwise in a neuron-like manner into an associative memory as a predictive process model, the control strategy planned via this model by optimization of a goal oriented performance index is then trained in the same way into a second associative memory. As a general mapping the learned behaviour is in both cases in general nonlinear, and by this such a control design is especially suited for strongly nonlinear processes. Simulations demonstrate the applicability of the new control concept

Towards Constructing and Using Selforganizing Visual Environment Representations for Mobile Robots

Due to the upcoming applications in the field of service robotics mobile robots are currently receiving increasing attention in industry and the scientific community. Applications in the area of service robotics demand a high degree of system autonomy, which robots without learning capabilities will not be able to meet. Learning is required in the context of action models and appropriate perception procedures. Both are extremely difficult to acquire especially with high bandwidth sensors (e.g. video cameras) which are needed in the envisioned unstructured worlds. Selflocalization is a basic requirement for mobile robots. This paper therefore proposes a new methodology for image based selflocalization using a selforganized visual representation of the environment. It allows for the seamless integration of active and passive localization

CAD of the Horowitz/Sidi-Design for Feedback Systems with Large Plant Parameter Uncertainty

I. M. Horowitz and M. Sidi (1972) presented a design procedure which guarantees quantitative demands on disturbance rejection and suppression of plant variation using the minimum controller gain just necessary for this effect. This paper describes an interactive, computer-aided implementation of this design procedure, which has proved to be very effective. The plant variations are handled by some expansions of a method from L. Longdon and D. J. East (1979), the controller design by a parameter optimization method using a vectorial performance criterion in an interactive manner

Computer Aided Controller Design for a Multiaxial Servohydraulic Vibration Test Bench with Large Parameter Uncertainties

In 1973 I. Horowitz and M. Sidi have given a method for quantitative design by cascaded control of processes with large parameter uncertainties. This paper indicates the necessary modifications in case of bandwidth limitations and the differences in the results, which can be reached in such a case. As an example the design of a car test bench - which motivated the investigations - is discussed

Methods for a Transparent Development and Optimization of Biotechnological Processes

The article at hand describes an integrating system for the intelligent control of complex bio technological processes including automatic modelling and model based control strategy generation. Starting with a summary of previously achieved results, some new approaches that provide better transparency to process engineers and operators are discussed. This includes aspects of self-organizing generation of structured dynamic nonlinear process models based upon the ideas of genetic programming as well as the transparent generation of fuzzy rules in a particular NeuroFuzzy approach. The latter is used for the classification of physiological states during batch and fed-batch fermentations and for the long time strategy generation to optimize the achievable product yield

Integration of Expert Systems and Neural Networks for the Control of Fermentation Processes

Expert systems and neural networks are new tools for the control of fermentation processes. With expert systems the fermentation plant and the process itself is modelled via a generalized, qualitative system description based on the experience of human experts. On the other hand neural networks and interpolating associative memories can learn the process behaviour directly by process observation. The paper at hand reports, how both control techniques can be combined for purposes like process supervision, modelling and optimization of biological plants

Minimum Information About a Simulation Experiment (MIASE)

The original publication is available at www.ploscompbiol.orgReproducibility of experiments is a basic requirement for science. Minimum Information (MI) guidelines have proved a helpful means of enabling reuse of existing work in modern biology. The Minimum Information Required in the Annotation of Models (MIRIAM) guidelines promote the exchange and reuse of biochemical computational models. However, information about a model alone is not sufficient to enable its efficient reuse in a computational setting. Advanced numerical algorithms and complex modeling workflows used in modern computational biology make reproduction of simulations difficult. It is therefore essential to define the core information necessary to perform simulations of those models. The Minimum Information About a Simulation Experiment describes the minimal set of information that must be provided to make the description of a simulation experiment available to others. It includes the list of models to use and their modifications, all the simulation procedures to apply and in which order, the processing of the raw numerical results, and the description of the final output. MIASE allows for the reproduction of any simulation experiment. The provision of this information, along with a set of required models, guarantees that the simulation experiment represents the intention of the original authors. Following MIASE guidelines will thus improve the quality of scientific reporting, and will also allow collaborative, more distributed efforts in computational modeling and simulation of biological processes.The discussions that led to the definition of MIASE benefited from the support of a Japan Partnering Award by the UK Biotechnology and Biological Sciences Research Council. DW was supported by the Marie Curie program and by the German Research Association (DFG Research Training School ‘‘dIEM oSiRiS’’ 1387/1). This publication is based on work (EJC) supported in part by Award No KUK-C1-013-04, made by King Abdullah University of Science and Technology (KAUST). FTB acknowledges support by the NIH (grant 1R01GM081070- 01). JC is supported by the European Commission, DG Information Society, through the Seventh Framework Programme of Information and Communication Technologies, under the VPH NoE project (grant number 223920). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Publishers versio