OCCS/en
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Observation and Control of Collaborative Systems |
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In Phase II, the project will investigate concepts and tools for the design of distributed observer/controller architectures. These are necessary in order to design more complex selforganising technical systems, which are at the same time safe, robust, and adaptive. In Phase I, we have developed a generic observer/controller architecture, which allows for controlled self-organisation in technical scenarios. By observing and analysing the current state of the system under observation and control (SuOC), this architecture can be used to prevent unwanted emergent behaviours and to encourage or enforce the desired ones. In Phase II, we will focus on complex distributed scenarios, which cannot be controlled by a single central observer/controller architecture. Therefore, our generic architecture will be extended into a distributed multi-level observer/controller architecture to achieve controllability of complex self-organising and adaptive technical systems. The potential of such a multi-layer observer/controller architecture for controlling complex distributed systems will be analysed systematically by theoretical analysis and experimental studies. We will explore the effect of different distributed observer/controller architectures on self-organisation, self-optimisation, and learning capabilities of the system. A major focus will be on identifying and analysing appropriate mechanisms for collective learning and cooperation within the technical system. In addition to some multi-agent scenarios, a major testbed for the validation of the project results will be the distributed application "Organic Traffic Control", which will be developed in the corresponding project OTC2. The generic results of OCCS are intended to be introduced into other projects of the priority programme.
from: 1 Juli 2007
until: 30 Juni 2009
Funding: DFG
Predecessing Project: QE
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
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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Clemens Lode, Urban Richter, Hartmut Schmeck
Adaption of XCS to Multi-Learner Predator/Prey Scenarios
In Martin Pelikan, Jürgen Branke, Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation (GECCO 2010), pages: 1015-1022, ACM, New York, NY, USA, Juli, 2010
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Urban Richter, Holger Prothmann, Hartmut Schmeck
Improving XCS Performance by Distribution
In Xiaodong Li, Michael Kirley, Mengjie Zhang, David Green, Vic Ciesielski, Hussein Abbass, Zbigniew Michalewicz, Tim Hendtlass, Kalyanmoy Deb, Kay Chen Tan, Jürgen Branke, and Yuhui Shi, Proceedings of the 7th International Conference on Simulated Evolution And Learning (SEAL 2008), pages: 111-120, Springer, LNCS, 5361, Dezember, 2008
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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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