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Seminar SS 10 Organic Computing

Join us at the Seminar SS 10 on Organic Computing, led by supervisor Thomas Ebi from the Chair for Embedded Systems at the University of Karlsruhe. Delve into exciting topics such as swarm intelligence, multi-agent learning systems, self-organization in neural networks, and autonomous robots. Discover the significance of self-X properties and agent-based computational economics. This seminar will provide insights into the collective behavior of decentralized systems and their real-world applications. Enhance your knowledge and engage in meaningful discussions with peers.

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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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