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Seminar SS 10 Organic Computing

Seminar SS 10 Organic Computing. Supervisor: Thomas Ebi Chair for Embedded Systems (CES) University of Karlsruhe. Topics. Seminar SS 10 Organic Computing. Swarm intelligence (overview) Learning in multi-agent systems Self-organizing in artificial neural networks

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Seminar SS 10 Organic Computing

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  1. Seminar SS 10Organic Computing Supervisor: Thomas Ebi Chair for Embedded Systems (CES) University of Karlsruhe

  2. Topics Seminar SS 10Organic Computing • Swarm intelligence (overview) • Learning in multi-agent systems • Self-organizing in artificial neural networks • Self-organization in autonomous robots • Self-organization in wireless sensor networks • ACE: Agent-based Computational Economics • Supervisor: Thomas Ebi Chair for Embedded Systems (CES)

  3. What is Organic Computing? Merriam Webster Dictionary on „organic“: • of, relating to, or derived from living organisms • having the characteristics of an organism : developing in the manner of a living plant or animal • forming an integral element of a whole • having systematic coordination of parts Learning from nature The whole is more than the sum of its parts

  4. What is Organic Computing? • Self-X Properties (“Autonomic Computing” IBM) • Self-Organization • Self-Configuration • Self-Optimization • Self-Healing • Self-Protection

  5. Swarm Intelligence • Collective behavior in decentralized, self-organized systems • Particle Swarm Optimization • Ant Colony Algorithm [ M Dorigo (Hrsg). Ant colony optimization and swarm intelligence. 5th International Workshop, ANTS (2006) ] [ RC Eberhart, Y Shi. Particle Swarm Optimization: Developments, Applications and Resources. CEC (2001). ] [ V Maniezzo, A Carbonaro. Ant Colony Optimization: an Overview. Essays and Surveys in Metaheuristics (2001). ] [ P Svenson et al. Swarm Intelligence for logistics: Background. Technical report (2004).]

  6. Multi-Agent Systems • Autonomous acting entities (agents) working together to reach a given goal [ M Wiering et al. Learning in Multi-Agent Sytems. (2000). ] [ L Panait, S Luke. Cooperative Multi-Agent Learning: The State of the Art. (2005). ] [ M Wooldridge. An Introductionto Multiagent Systems. John WileyandSons Ltd (2002). ] [ MS Greenberg et al. Mobile Agentsand Security. (1998) . ]

  7. Evolutionary Algorithms • Four major paradigms • Genetic Algorithms • Genetic Programming • Evolutionary Programming • Evolutionary Strategies [ Darrell Whitley. An Overview of Evolutionary Algorithms: Practical Issues and Common Pitfalls. (2001). ] [ PJ Fleming, RC Purshouse. Evolutionary algorithms in control systems engineering: a survey. Control Engineering Practice 10:1223–1241 (2002). ]

  8. Paper and Presentation • Paper • LaTeX and Word Templates • 10 pages • In German or English • Correct scientific writing (structure, references, …) • typos, duplicate words, … are avoidable • Presentation • 30 minutes (25 minutes + 5 minutes for questions) • Projector is available  PowerPoint, OpenOffice, PDF

  9. Literature Research • Reading paper references • Search engines, e.g. Google, Yahoo, and so on • Wikipedia • Not to be referenced in the paper • Paper search engine http://scholar.google.com • University library • Journal papers via “Elektronische Zeitschiftenbibliothek” • Portals • ACM • IEEE Xplore • DBLP

  10. Dates and Deadlines • July 17 End of lectures • ~July 19 Presentation II (if necessary) • ~July 19 Presentation I • July 12 Slides have to be finished • June 28 Preliminary final version of slides • June 21 Paper has to finished • June 14 Preliminary final version of paper • May 24 First version of paper • May 10 Structure of paper • May 4 First ideas, read papers

  11. Topics • Swarm intelligence (overview) • Learning in multi-agent systems • Self-organizing in artificial neural networks • Self-organization in autonomous robots • Self-organization in wireless sensor networks • ACE: Agent-based Computational Economics

  12. Topics • Swarm intelligence (overview) • Basics of swarm intelligence • Emergence, self-x properties • Overview of different approaches/algorithms • Ant colony, particle swarm, etc. • Starting point: http://en.wikipedia.org/wiki/Swarm_intelligence • Learning in multi-agent systems • Decentralized learning algorithms • “Intelligent agents” http://en.wikipedia.org/wiki/Intelligent_agent • Social learning

  13. Topics • Self-organizing in artificial neural networks • Biological inspiration • Unsupervised learning techniques • E.g. Self organizing maps SOM • Self-organization in autonomous robots • Challenges of autonomous robots • Motion planning • Target tracking • Etc. • Solutions • Robotic projects of the SPP http://organic-computing.de/spp

  14. Topics • Self-organization in wireless sensor networks • Route finding • Energy efficiency • Sensor network projects of the SPP http://organic-computing.de/spp • ACE: Agent-based Computational Economics • http://www.econ.iastate.edu/tesfatsia/ace.htm • http://en.wikipedia.org/wiki/Agent-Based_Computational_Economics

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