Telecom Paris Dep. Informatique & Réseaux J-L. Dessalles ← Home page December 2020

U.E. LUTECE TP-09

    Emergence in Complex Systems
     From nature to engineering

                            →    other AI courses

         

    
→    [ Read the General presentation of the course ]

Topics

The course is divided in several chapters corresponding to lectures. Each chapter proposes several topics. Don’t hesitate to skip some of them and go directly to those that match your interests best.                  
→    List of students (you may upload a photo if you wish)

→    2020 Evaluation: possible answers

→    [Docs, Biblio & Links] (below)

Forum

Personal Work

1. Your answers during the Lab Work sessions are recorded.
2. You are expected to make a personal contribution (project) during the week.

Your Project

Your project will typically consist of an improvement on some topic studied during the Lab Work sessions. You should pick a problem that you want to investigate further. Initiative is welcome.

1. You may choose one of the "Suggestions for further work" mentioned in the lab sessions, but you are free to choose any topic that you regard as relevant.
2. Your study should be based on simulation results.
3. You should seek for simple and clear-cut results from which we can learn something (even if you study is inconclusive, we want to know clearly why). Initiative and logical clarity will be appreciated.
4. Try to be realistic about what you can do. Your study should not be trivial, and it should lead somewhere.
5. Your contribution shoud be achieved using the Evolife platform. If it involves code, this code should be in Python.

You are encouraged to work in pairs. In this case,

1. both partners should enter the same project (title, description) on the site.
2. Both members of the pair should be heard during the Friday presentation.
3. If you opt for a common report, then the report should include two separate parts that make clear who did what.

Please indicate here what you intend to do as a project.
    You may decide as soon as Tuesday evening.
    If you change your mind, redo the inscription.
    →    You may consult the others’ projects. Try to play a minority game in your choice!

On Thursday evening :

• Please upload a few slides (typically three) that illustrate your work (.pdf or .ppt or .pptx; openoffice should also be ok).
  Try to be visual, avoid text (please avoid bullet lists!).
  DON’T SEND ANYTHING THROUGH EMAIL. Use the upload program.
  To capture images from Evolife, use the [Photo] button (or [P] shortcut). To make movies, press [V] to enter the film (or video) mode.
  Images are stored in ___Result; you have to assemble them to make a movie (see Evolife documentation). Avoid embedding movies into .pptx, or only as gif images.
• Please upload additional relevant material, such as:
  • A python code file, typically a modified Evolife scenario) for the record.
  • A short text presenting what you achieved (problem, solution, results, links to references) (and again, don’t send any file through email)

On Friday

3. You will be ask to talk during 3 minutes about your small study (from you seat). Your audience is not the teachers, but the other students.

1. Be interesting
2. Be scientifically sound    

4. You will be asked to answer a small quiz in English (~ 20 min.)

Report

1. The code and the written description might be uploaded until the next Tuesday after the LUTECE week (in the evening).

Docs

• Pierre-Alexandre Murena‘s slides on Evolutionary Game Theory.
• Complexity uses in various fields (by Sorin Solomon)
• Genetic Algorithms (JL Dessalles)
• Emergence - Examples and definition (JL Dessalles)
• Emergence of Communication by Pr.Takashi Hashimoto
• Swarm Intelligence (JL Dessalles)
• Morphogenesis
• Small worlds (Pierre Senellart)
• Structure and models of real-world graphs and networks (Jure Leskovec)

Bibliography

• Amblard, F. & Phan, D. (2007). Agent-based modelling and simulation in the social and human sciences. Oxford: The Bardwell-Press.
• Bonabeau, E., Dorigo, M. & Theraulaz, G. (1999). Swarm intelligence: from natural to artificial systems. Oxford: Oxford University Press.
• Camazine, S., Deneubourg, J.-l., Franks, N. R., Sneyd, J., Theraulaz, G. & Bonabeau, E. (2001). Self-organization in biological systems. Princeton, NJ: Princeton University Press.
• Dessalles, J.-L. (1996). L’ordinateur génétique. Paris: Hermes Science.
• Dessalles, J.-L., Gaucherel, C. & Gouyon, P.-H. (2016). Le fil de la vie - La face immatérielle du vivant. Paris: Odile Jacob.
• Goldberg, D. E. (1989). Genetic algorithms in search, optimization and machine learning. Reading (MA): Addison Wesley Publishing Company.
• Easley, D. & Kleinberg, J. (2010). Networks, crowds, and markets: Reasoning about a highly connected world. Cambridge University Press.
• Goldberg, D. E. (1989). Algorithmes génétiques - Exploration, optimisation et apprentissage automatique. Paris: Addison Wesley France, ed. 1994.
• Hansell, M. (2007). Built by animals. Oxford, UK: Oxford University Press.
• Holland, J. H. (1975). Adaptation in natural and artificial systems. Cambridge, MA: MIT Press, ed. 1992.
• Kauffman, S. (1993). The origins of order: self-organization and selection in evolution. Oxford university press.
• Nicolis, G. & Nicolis, C. (2012). Foundations of complex systems: emergence, information and predicition. Singapour: World Scientific.
• Rennard, J.-P. (2002). Vie artificielle - Où la biologie rencontre l’informatique. Paris: Vuibert.
• Steeb, W.-H. (2008). The nonlinear workbook (5th ed.). Singapore: World Scientific.

Links

• Urban, T. (2019). A game of giants. Wait but why.
• Helbig, R. (2016). Four misconceptions about evolution. K!nea Media.
• Complex systems on YouTube
• Bibliography on evolutionary computation