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Distributed intelligent systems

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Distributed intelligent systems

ENG-466

Lecturer(s) :

Martinoli Alcherio

Language:

English

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 using simulation and real hardware platforms.

Content

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: 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.
Make an oral presentation.
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 a course project involving teamwork

Expected student activities

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

Assessment methods

Continuous control (50%) with final oral exam (50%).

Supervision

Office hours	Yes
Assistants	Yes
Forum	Yes

Resources

Bibliography

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

Websites

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

Moodle Link

https://moodle.epfl.ch/course/view.php?id=15472

Prerequisite for

R&D activities in engineering

In the programs

Environmental Sciences and Engineering, 2020-2021, Master semester 2
- Semester
  Spring
- Exam form
  Oral
- Credits
  5
- Subject examined
  Distributed intelligent systems
- Lecture
  2 Hour(s) per week x 14 weeks
- Exercises
  3 Hour(s) per week x 14 weeks
Environmental Sciences and Engineering, 2020-2021, Master semester 4
- Semester
  Spring
- Exam form
  Oral
- Credits
  5
- Subject examined
  Distributed intelligent systems
- Lecture
  2 Hour(s) per week x 14 weeks
- Exercises
  3 Hour(s) per week x 14 weeks
Civil Engineering, 2020-2021, Master semester 2
- Semester
  Spring
- Exam form
  Oral
- Credits
  5
- Subject examined
  Distributed intelligent systems
- Lecture
  2 Hour(s) per week x 14 weeks
- Exercises
  3 Hour(s) per week x 14 weeks
Civil Engineering, 2020-2021, Master semester 4
- Semester
  Spring
- Exam form
  Oral
- Credits
  5
- Subject examined
  Distributed intelligent systems
- Lecture
  2 Hour(s) per week x 14 weeks
- Exercises
  3 Hour(s) per week x 14 weeks
Communication Systems - master program, 2020-2021, Master semester 2
- Semester
  Spring
- Exam form
  Oral
- Credits
  5
- Subject examined
  Distributed intelligent systems
- Lecture
  2 Hour(s) per week x 14 weeks
- Exercises
  3 Hour(s) per week x 14 weeks
Communication Systems - master program, 2020-2021, Master semester 4
- Semester
  Spring
- Exam form
  Oral
- Credits
  5
- Subject examined
  Distributed intelligent systems
- Lecture
  2 Hour(s) per week x 14 weeks
- Exercises
  3 Hour(s) per week x 14 weeks
Computational science and Engineering, 2020-2021, Master semester 2
- Semester
  Spring
- Exam form
  Oral
- Credits
  5
- Subject examined
  Distributed intelligent systems
- Lecture
  2 Hour(s) per week x 14 weeks
- Exercises
  3 Hour(s) per week x 14 weeks
Computational science and Engineering, 2020-2021, Master semester 4
- Semester
  Spring
- Exam form
  Oral
- Credits
  5
- Subject examined
  Distributed intelligent systems
- Lecture
  2 Hour(s) per week x 14 weeks
- Exercises
  3 Hour(s) per week x 14 weeks
Computer Science - Cybersecurity, 2020-2021, Master semester 2
- Semester
  Spring
- Exam form
  Oral
- Credits
  5
- Subject examined
  Distributed intelligent systems
- Lecture
  2 Hour(s) per week x 14 weeks
- Exercises
  3 Hour(s) per week x 14 weeks
Computer Science - Cybersecurity, 2020-2021, Master semester 4
- Semester
  Spring
- Exam form
  Oral
- Credits
  5
- Subject examined
  Distributed intelligent systems
- Lecture
  2 Hour(s) per week x 14 weeks
- Exercises
  3 Hour(s) per week x 14 weeks
Computer Science, 2020-2021, Master semester 2
- Semester
  Spring
- Exam form
  Oral
- Credits
  5
- Subject examined
  Distributed intelligent systems
- Lecture
  2 Hour(s) per week x 14 weeks
- Exercises
  3 Hour(s) per week x 14 weeks
Computer Science, 2020-2021, Master semester 4
- Semester
  Spring
- Exam form
  Oral
- Credits
  5
- Subject examined
  Distributed intelligent systems
- Lecture
  2 Hour(s) per week x 14 weeks
- Exercises
  3 Hour(s) per week x 14 weeks
Data Science, 2020-2021, Master semester 2
- Semester
  Spring
- Exam form
  Oral
- Credits
  5
- Subject examined
  Distributed intelligent systems
- Lecture
  2 Hour(s) per week x 14 weeks
- Exercises
  3 Hour(s) per week x 14 weeks
Data Science, 2020-2021, Master semester 4
- Semester
  Spring
- Exam form
  Oral
- Credits
  5
- Subject examined
  Distributed intelligent systems
- Lecture
  2 Hour(s) per week x 14 weeks
- Exercises
  3 Hour(s) per week x 14 weeks
Electrical and Electronics Engineering, 2020-2021, Master semester 2
- Semester
  Spring
- Exam form
  Oral
- Credits
  5
- Subject examined
  Distributed intelligent systems
- Lecture
  2 Hour(s) per week x 14 weeks
- Exercises
  3 Hour(s) per week x 14 weeks
Electrical and Electronics Engineering, 2020-2021, Master semester 4
- Semester
  Spring
- Exam form
  Oral
- Credits
  5
- Subject examined
  Distributed intelligent systems
- Lecture
  2 Hour(s) per week x 14 weeks
- Exercises
  3 Hour(s) per week x 14 weeks
Energy Management and Sustainability, 2020-2021, Master semester 2
- Semester
  Spring
- Exam form
  Oral
- Credits
  5
- Subject examined
  Distributed intelligent systems
- Lecture
  2 Hour(s) per week x 14 weeks
- Exercises
  3 Hour(s) per week x 14 weeks
Energy Management and Sustainability, 2020-2021, Master semester 4
- Semester
  Spring
- Exam form
  Oral
- Credits
  5
- Subject examined
  Distributed intelligent systems
- Lecture
  2 Hour(s) per week x 14 weeks
- Exercises
  3 Hour(s) per week x 14 weeks
Energy Science and Technology, 2020-2021, Master semester 2
- Semester
  Spring
- Exam form
  Oral
- Credits
  5
- Subject examined
  Distributed intelligent systems
- Lecture
  2 Hour(s) per week x 14 weeks
- Exercises
  3 Hour(s) per week x 14 weeks
Energy Science and Technology, 2020-2021, Master semester 4
- Semester
  Spring
- Exam form
  Oral
- Credits
  5
- Subject examined
  Distributed intelligent systems
- Lecture
  2 Hour(s) per week x 14 weeks
- Exercises
  3 Hour(s) per week x 14 weeks
Microengineering, 2020-2021, Master semester 2
- Semester
  Spring
- Exam form
  Oral
- Credits
  5
- Subject examined
  Distributed intelligent systems
- Lecture
  2 Hour(s) per week x 14 weeks
- Exercises
  3 Hour(s) per week x 14 weeks
Microengineering, 2020-2021, Master semester 4
- Semester
  Spring
- Exam form
  Oral
- Credits
  5
- Subject examined
  Distributed intelligent systems
- Lecture
  2 Hour(s) per week x 14 weeks
- Exercises
  3 Hour(s) per week x 14 weeks
Robotics, 2020-2021, Master semester 2
- Semester
  Spring
- Exam form
  Oral
- Credits
  5
- Subject examined
  Distributed intelligent systems
- Lecture
  2 Hour(s) per week x 14 weeks
- Exercises
  3 Hour(s) per week x 14 weeks
Robotics, 2020-2021, Master semester 4
- Semester
  Spring
- Exam form
  Oral
- Credits
  5
- Subject examined
  Distributed intelligent systems
- Lecture
  2 Hour(s) per week x 14 weeks
- Exercises
  3 Hour(s) per week x 14 weeks
Biocomputing minor, 2020-2021, Spring semester
- Semester
  Spring
- Exam form
  Oral
- Credits
  5
- Subject examined
  Distributed intelligent systems
- Lecture
  2 Hour(s) per week x 14 weeks
- Exercises
  3 Hour(s) per week x 14 weeks
Robotics, Control and Intelligent Systems (edoc), 2020-2021
- Semester
  Spring
- Exam form
  Oral
- Credits
  5
- Subject examined
  Distributed intelligent systems
- Lecture
  2 Hour(s) per week x 14 weeks
- Exercises
  3 Hour(s) per week x 14 weeks

Reference week

	Tu	Th
8-9		GRA330 GRB001 GRC002
9-10	GRA331
10-11	GRA331
11-12
12-13
13-14
14-15
15-16
16-17
17-18
18-19
19-20
20-21
21-22

Lecture

Exercise, TP

Project, other

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Spring semester
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