1 / 10

Seminar WS 10/11 Organic Computing

Seminar WS 10/11 Organic Computing. Supervisor: Thomas Ebi Chair for Embedded Systems (CES) KIT – Karlsruhe Institute of Technology. What is Organic Computing?. Merriam Webster Dictionary on „organic“: of, relating to, or derived from living organisms

Rita
Download Presentation

Seminar WS 10/11 Organic Computing

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Seminar WS 10/11Organic Computing Supervisor: Thomas Ebi Chair for Embedded Systems (CES) KIT – Karlsruhe Institute of Technology

  2. 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

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

  4. 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).]

  5. 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) . ]

  6. 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). ]

  7. Paper and Presentation • Paper • LaTeX and Word Templates • 10-12 pages • In German or English • Correct scientific writing (structure, references, …) • typos, duplicate words, … are avoidable • Presentation • 25 minutes • Projector is available  PowerPoint, OpenOffice, PDF

  8. 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

  9. Dates and Deadlines • Feb. 12 End of lectures • Feb., Week 1 Presentation II (if necessary) • Feb., Week 1 Presentation I • Jan., Week 4 Slides have to be finished • Jan., Week 3 Preliminary final version of slides • Jan., Week 2 Paper has to finished • Dec., Week 2 Preliminary final version of paper • Nov., Week 4 First version of paper • Nov., Week 2 Structure of paper • Nov., Week 1 First ideas, read papers

  10. Topics • Artificial Immune System • Agent-based Resource Value Estimation (CARVE) • Context Discovery through Particle Swarm Optimization • Analysing Emergence using Numerical Methods • Traffic Reduction in Multi-Broker Publish-Subscribe Systems • Resource Negotiation Infrastructure • Scheduling and Mapping Games for Adaptive Distributed Systems • Nature-Inspired Routing (AntHocNet) • Intelligent agents for Energy Optimization in Power Networks • Distributed Troubleshooting Agents

More Related