Distributed Intelligent Systems
A number of natural and artificial systems can be considered as intrinsically distributed and consisting of nodes presenting a certain degree of intelligence. Typical examples of distributed intelligent systems include social insect colonies, flocks of vertebrates, multi-agent systems, transportation systems, multi-robot systems, and wireless sensor networks. The goals of this course are two-fold: first, to provide students with a sufficient mathematical and computational background to analyze distributed intelligent systems through appropriate models, and second, to illustrate several coordination strategies and show how to concretely implement and optimize them. The course is a well-balanced mixture of theory and laboratory exercises using simulation and real hardware platforms.
It involves the following topics:
- Introduction to key concepts such as self-organization and software and hardware tools used in the course.
- Examples of natural, artificial, and hybrid distributed intelligent systems.
- Modeling methods: microscopic and macroscopic, multi-level; spatial and non-spatial; mean field and stochastic approaches.
- Machine-learning methods: single- and multi-agent techniques; expensive optimization problems and noise resistance.
- Coordination strategies and distributed control: direct and indirect schemes; communication channels and cost; distributed sensing and action; performance evaluation.
Announcements and Additional Information
Please send questions and concerns to the TA mailing list:
Office hours: by appointment only. Please send your time availability and discussion subject to the TA mailing list. A TA will respond you directly and set-up an appointment for discussion.
Lucas Waelti (Head TA)
Chiara Ercolani (TA)
Wanting Jin (TA)
I. Kagan Erünsal (TA)
The support staff will be involved in the testing of exercises and the course project.
Tifanny Portela (Help TA)
Lavinia Schlyter (Help TA)
First of all, we would like to acknowledge all the students attending the Swarm Intelligence course between 2004 and 2009: their extremely constructive feedback has been incorporated in the substantially redesigned edition of the course, correspondingly renamed Distributed Intelligent Systems.
Second, we would like to acknowledge the EPFL-FIFO program (Fonds d’Innovation pour la FOrmation) for having supported two major tool development projects for this course: “Simulations Interactives en Robotique Mobile” and “E-puck: robot mobile de table pour une éducation interdisciplinaire”.
Third, we would like to acknowledge Prof. Guy Theraulaz for sharing with us lecture notes from his course on Collective Intelligence in Biological Societies; Prof. Francesco Mondada and Dr. Olivier Michel for all the development and discussions around the real and simulated e-puck robot and, more generally, educational tools for mobile robotics; and several other scientists for sharing with us additional teaching material for this edition of the course (in alphabetical order): Dr. Guillermo Barrenetxea, Dr. Maurice Clerc, Prof. Jean-Louis Deneubourg, Prof. Marco Dorigo, Prof. Deborah Estrin, Dr. Nidhi Kalra, and Prof. Roland Siegwart.
Finally, we would like to thank Cyberbotics Ltd., for providing the high-fidelity robotics simulator used in the course.