Distributed intelligent systems

Summary

The goal of this course is to provide methods and tools for modeling distributed intelligent systems as well as designing and optimizing coordination strategies. The course is a well-balanced mixture of theory and practical activities.

Content

Keywords

Swarm intelligence, artificial intelligence, machine-learning, distributed robotics, swarm robotics, multi-robot systems, sensor networks, modeling, control, optimization

Learning Prerequisites

Required courses

Fundamentals in analysis, probability, and programming for both compiled and interpreted languages

Recommended courses

Basic knowledge in statistics, programming language used in the course (C, Matlab, Python), and signals and systems

Learning Outcomes

By the end of the course, the student must be able to:

• Design control algorithms
• Formulate a model at different level of abstraction for a distributed intelligent system
• Analyze a model of a distributed intelligent system
• Analyze a distributed coordination strategy/algorithm
• Design a distributed coordination strategy/algorithm
• Implement code for single robot and multi-robot systems
• Carry out systematic performance evaluation of a distributed intelligent system
• Apply modeling and design methods to specific problems requiring distributed sensing and action
• Optimize a controller or a set of possibly coordinated controllers using model-based or data-driven methods

Transversal skills

• Demonstrate a capacity for creativity.
• Access and evaluate appropriate sources of information.
• Collect data.
• Plan and carry out activities in a way which makes optimal use of available time and other resources.
• Write a scientific or technical report.
• Evaluate one's own performance in the team, receive and respond appropriately to feedback.

Teaching methods

Ex-cathedra lectures, assisted exercises, and course project in teams

Expected student activities

Attending lectures, carrying out exercises and the course project, and reading handouts.

Assessment methods

Oral exam (50%) with continuous assessment during the semester based on course project (50%).

Supervision

Resources

Virtual desktop infrastructure (VDI)

Yes

Bibliography

Lecture notes, selected papers and book chapters distributed at each lecture.

Websites

• https://disal.epfl.ch/teaching/distributed_intelligent_systems/

Moodle Link

• https://go.epfl.ch/ENG-466

Videos

• https://mediaspace.epfl.ch/channel/ENG-466+Distributed+intelligent+systems+-+Spring+2022/29956

Prerequisite for

R&D activities in engineering