QE/en
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Quantitative Emergence |
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This joint project will focus on the design of concepts and tools for the implementation of an architecture needed for the realisation of self-organising technical systems which are, at the same time reliable, robust, and adaptive. As an important prerequisite for designing such systems we have to understand the effects of emergent global behaviour in networks of intelligent autonomous units (i. e., we have to quantify emergent behaviour) and we needtools to prevent unwanted behaviour and to encourage or enforce desired positive effects. The architecture will consist of a network of autonomous units (called production system), one or more observers, and one or more controllers. We shall develop an appropriate methodology for observing the (global) behaviour of the system and for quantifying and evaluating emergence effects. Furthermore, we have to generate adequate responses of the controller to the results of the observer in order to enable a controlled emergent behaviour within the restrictions set by some external unit (the environment). This requires an exploration of various potential ways of influencing the behaviour of a selforganising production system. For the initial 2 years of this project we shall abstract from realistic technical applications and will use rather simple artificial production systems exhibiting some interesting properties. Only later, during Phase II (i. e., years 3 and 4) we intend to combine the new concepts and tools for the observer and the controller with the more complex organic production system organic traffic control (OTC), which will be developed in the corresponding project OTC.
from: 1 Juli 2005
until: 30 Juni 2007
Funding: DFG
Evolutionary Computing, Organic Computing, Machine Learning, Global Optimization, Evolutionary Strategies, Genetic Algorithms, Evolutionary Optimization Of Dynamic Problems, Nature-inspired Algorithms, Human Computer Systems
Inhaltsverzeichnis
article
Hartmut Schmeck, Christian Müller-Schloer, Emre Cakar, Moez Mnif, Urban Richter
Adaptivity and Self-Organisation in Organic Computing Systems
ACM Transactions on Autonomous and Adaptive Systems (TAAS), 5, (3), pages 10:1-10:32, September, 2010
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inproceedings
Jürgen Branke, Moez Mnif, Christian Müller-Schloer, Holger Prothmann, Urban Richter, Fabian Rochner, Hartmut Schmeck
Organic Computing - Addressing Complexity by Controlled Self-Organization
In Tiziana Margaria, Anna Philippou, and Bernhard Steffen, Post-Conference Proceedings of the 2nd International Symposium on Leveraging Applications of Formal Methods, Verification and Validation (ISoLA 2006), pages: 185-191, IEEE, November, 2006
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Emre Cakar, Moez Mnif, Christian Müller-Schloer, Urban Richter, Hartmut Schmeck
Towards a Quantitative Notion of Self-Organisation
Proceedings of the 2007 IEEE Congress on Evolutionary Computation (CEC 2007), pages: 4222-4229September, 2007
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Moez Mnif, Urban Richter, Jürgen Branke, Hartmut Schmeck, Christian Müller-Schloer
Measurement and Control of Self-Organised Behaviour in Robot Swarms
In Paul Lukowicz, Lothar Thiele, and Gerhard Tröster, Proceedings of the 20th International Conference on Architecture of Computing Systems (ARCS 2007), pages: 209-223, Springer, LNCS, 4415, März, 2007
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Oliver Ribock, Urban Richter, Hartmut Schmeck
Using Organic Computing to Control Bunching Effects
In Uwe Brinkschulte, Theo Ungerer, Christian Hochberger, and Rainer G. Spallek, Proceedings of the 21th International Conference on Architecture of Computing Systems (ARCS 2008), pages: 232-244, Springer, LNCS, 4934, Februar, 2008
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Urban Richter, Moez Mnif, Jürgen Branke, Christian Müller-Schloer, Hartmut Schmeck
Towards a Generic Observer/Controller Architecture for Organic Computing
In Christian Hochberger and Rüdiger Liskowsky, INFORMATIK 2006 – Informatik für Menschen!, pages: 112-119, Bonner Köllen Verlag, LNI, P-93, Oktober, 2006
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Urban Richter, Moez Mnif
Learning to Control the Emergent Behaviour of a Multi-Agent System
In Franziska Klügl, Karl Tuyls, and Sandip Sen, Proceedings of the 2008 Workshop on Adaptive Learning Agents and Multi-Agent Systems at AAMAS 2008 (ALAMAS+ALAg 2008), pages: 33-40Mai, 2008
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book
Christian Müller-Schloer, Hartmut Schmeck, Theo Ungerer
Organic Computing - A Paradigm Shift for Complex Systems
Birkhäuser, Juni, 2011
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Urban Richter
Controlled Self-Organisation Using Learning Classifier Systems
KIT Scientific Publishing, November, 2009
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phdthesis
Urban Richter
Controlled Self-Organisation Using Learning Classifier Systems
Hartmut Schmeck; Karl-Heinz Waldmann, 2009/07/30, PhD thesis at the Universität Karlsruhe (TH), Fakultät für Wirtschaftswissenschaften
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