Distributed intelligent systems

ENG-466 / 5 credits

Teacher:

Language: English

Remark: Pas donné en 2023-24

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

 

  • Introduction to key concepts such as self-organization and tools used in the course
  • Examples of natural, artificial and hybrid distributed intelligent systems
  • Modeling methods: sub-microscopic, microscopic, macroscopic, multi-level; spatial and non-spatial; mean field, approximated and exact approaches
  • Machine-learning methods: single- and multi-agent techniques; expensive optimization problems and noise resistance
  • Coordination strategies and distributed control: direct and indirect schemes; algorithms and methods; performance evaluation
  • Application examples in distributed sensing and action

 

Keywords

Artificial intelligence, swarm intelligence, distributed robotics, sensor networks, modeling, machine-learning, control

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 homework in team

Expected student activities

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

Assessment methods

Oral exam (60%) with continuous assessment during the semester (40%).

Supervision

Office hours Yes
Assistants Yes
Forum Yes

Resources

Bibliography

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

Websites

Moodle Link

Prerequisite for

R&D activities in engineering

In the programs

  • Semester: Spring
  • Exam form: Oral (summer session)
  • Subject examined: Distributed intelligent systems
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 3 Hour(s) per week x 14 weeks
  • Type: optional
  • Semester: Spring
  • Exam form: Oral (summer session)
  • Subject examined: Distributed intelligent systems
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 3 Hour(s) per week x 14 weeks
  • Type: optional
  • Semester: Spring
  • Exam form: Oral (summer session)
  • Subject examined: Distributed intelligent systems
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 3 Hour(s) per week x 14 weeks
  • Type: optional
  • Semester: Spring
  • Exam form: Oral (summer session)
  • Subject examined: Distributed intelligent systems
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 3 Hour(s) per week x 14 weeks
  • Type: optional
  • Semester: Spring
  • Exam form: Oral (summer session)
  • Subject examined: Distributed intelligent systems
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 3 Hour(s) per week x 14 weeks
  • Type: optional
  • Semester: Spring
  • Exam form: Oral (summer session)
  • Subject examined: Distributed intelligent systems
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 3 Hour(s) per week x 14 weeks
  • Type: optional
  • Semester: Spring
  • Exam form: Oral (summer session)
  • Subject examined: Distributed intelligent systems
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 3 Hour(s) per week x 14 weeks
  • Type: optional
  • Semester: Spring
  • Exam form: Oral (summer session)
  • Subject examined: Distributed intelligent systems
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 3 Hour(s) per week x 14 weeks
  • Type: optional
  • Semester: Spring
  • Exam form: Oral (summer session)
  • Subject examined: Distributed intelligent systems
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 3 Hour(s) per week x 14 weeks
  • Type: optional
  • Semester: Spring
  • Exam form: Oral (summer session)
  • Subject examined: Distributed intelligent systems
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 3 Hour(s) per week x 14 weeks
  • Type: optional
  • Semester: Spring
  • Exam form: Oral (summer session)
  • Subject examined: Distributed intelligent systems
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 3 Hour(s) per week x 14 weeks
  • Type: optional
  • Semester: Spring
  • Exam form: Oral (summer session)
  • Subject examined: Distributed intelligent systems
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 3 Hour(s) per week x 14 weeks
  • Type: optional
  • Semester: Spring
  • Exam form: Oral (summer session)
  • Subject examined: Distributed intelligent systems
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 3 Hour(s) per week x 14 weeks
  • Type: optional
  • Semester: Spring
  • Exam form: Oral (summer session)
  • Subject examined: Distributed intelligent systems
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 3 Hour(s) per week x 14 weeks
  • Type: optional
  • Semester: Spring
  • Exam form: Oral (summer session)
  • Subject examined: Distributed intelligent systems
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 3 Hour(s) per week x 14 weeks
  • Type: optional
  • Semester: Spring
  • Exam form: Oral (summer session)
  • Subject examined: Distributed intelligent systems
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 3 Hour(s) per week x 14 weeks
  • Type: optional
  • Semester: Spring
  • Exam form: Oral (summer session)
  • Subject examined: Distributed intelligent systems
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 3 Hour(s) per week x 14 weeks
  • Type: optional
  • Semester: Spring
  • Exam form: Oral (summer session)
  • Subject examined: Distributed intelligent systems
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 3 Hour(s) per week x 14 weeks
  • Type: optional
  • Semester: Spring
  • Exam form: Oral (summer session)
  • Subject examined: Distributed intelligent systems
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 3 Hour(s) per week x 14 weeks
  • Type: optional
  • Semester: Spring
  • Exam form: Oral (summer session)
  • Subject examined: Distributed intelligent systems
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 3 Hour(s) per week x 14 weeks
  • Type: optional
  • Semester: Spring
  • Exam form: Oral (summer session)
  • Subject examined: Distributed intelligent systems
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 3 Hour(s) per week x 14 weeks
  • Type: optional
  • Semester: Spring
  • Exam form: Oral (summer session)
  • Subject examined: Distributed intelligent systems
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 3 Hour(s) per week x 14 weeks
  • Type: optional
  • Semester: Spring
  • Exam form: Oral (summer session)
  • Subject examined: Distributed intelligent systems
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 3 Hour(s) per week x 14 weeks
  • Type: optional
  • Exam form: Oral (summer session)
  • Subject examined: Distributed intelligent systems
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 3 Hour(s) per week x 14 weeks
  • Type: optional

Reference week

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