Distributed intelligent systems

ENG-466 / 5 credits

Teacher: 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.

Content

- Introduction to key concepts (e.g., self-organization, stigmergy) 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
- Data-driven methods: in-line learning and metaheuristic optimization; expensive optimization problems and noise resistance; performance evaluation
- Coordination strategies and distributed control algorithms
- Application examples in distributed sensing and action

Keywords

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

Learning Prerequisites

Required courses

Fundamentals in analysis, probability and statistics, and programming for both compiled and interpreted languages (C, Matlab, Python are used in the course).

Recommended courses

Signals and systems, continuous-time and discrete-time linear control systems

Learning Outcomes

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

  • Design simple control algorithms for single robots
  • 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
  • Optimize controllers and/or coordination strategies 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.
  • Make an oral presentation.
  • Manage priorities.

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

Office hours Yes
Assistants Yes
Forum Yes

Resources

Virtual desktop infrastructure (VDI)

Yes

Bibliography

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

Websites

Moodle Link

Videos

Prerequisite for

R&D activities in engineering

In the programs

  • Semester: Fall
  • Exam form: Oral (winter session)
  • Subject examined: Distributed intelligent systems
  • Courses: 2 Hour(s) per week x 14 weeks
  • Project: 1 Hour(s) per week x 14 weeks
  • Lab: 2 Hour(s) per week x 14 weeks
  • Type: optional
  • Semester: Fall
  • Exam form: Oral (winter session)
  • Subject examined: Distributed intelligent systems
  • Courses: 2 Hour(s) per week x 14 weeks
  • Project: 1 Hour(s) per week x 14 weeks
  • Lab: 2 Hour(s) per week x 14 weeks
  • Type: optional
  • Semester: Fall
  • Exam form: Oral (winter session)
  • Subject examined: Distributed intelligent systems
  • Courses: 2 Hour(s) per week x 14 weeks
  • Project: 1 Hour(s) per week x 14 weeks
  • Lab: 2 Hour(s) per week x 14 weeks
  • Type: optional
  • Semester: Fall
  • Exam form: Oral (winter session)
  • Subject examined: Distributed intelligent systems
  • Courses: 2 Hour(s) per week x 14 weeks
  • Project: 1 Hour(s) per week x 14 weeks
  • Lab: 2 Hour(s) per week x 14 weeks
  • Type: optional
  • Semester: Fall
  • Exam form: Oral (winter session)
  • Subject examined: Distributed intelligent systems
  • Courses: 2 Hour(s) per week x 14 weeks
  • Project: 1 Hour(s) per week x 14 weeks
  • Lab: 2 Hour(s) per week x 14 weeks
  • Type: optional
  • Semester: Fall
  • Exam form: Oral (winter session)
  • Subject examined: Distributed intelligent systems
  • Courses: 2 Hour(s) per week x 14 weeks
  • Project: 1 Hour(s) per week x 14 weeks
  • Lab: 2 Hour(s) per week x 14 weeks
  • Type: optional
  • Semester: Fall
  • Exam form: Oral (winter session)
  • Subject examined: Distributed intelligent systems
  • Courses: 2 Hour(s) per week x 14 weeks
  • Project: 1 Hour(s) per week x 14 weeks
  • Lab: 2 Hour(s) per week x 14 weeks
  • Type: optional
  • Semester: Fall
  • Exam form: Oral (winter session)
  • Subject examined: Distributed intelligent systems
  • Courses: 2 Hour(s) per week x 14 weeks
  • Project: 1 Hour(s) per week x 14 weeks
  • Lab: 2 Hour(s) per week x 14 weeks
  • Type: optional
  • Semester: Fall
  • Exam form: Oral (winter session)
  • Subject examined: Distributed intelligent systems
  • Courses: 2 Hour(s) per week x 14 weeks
  • Project: 1 Hour(s) per week x 14 weeks
  • Lab: 2 Hour(s) per week x 14 weeks
  • Type: optional
  • Semester: Fall
  • Exam form: Oral (winter session)
  • Subject examined: Distributed intelligent systems
  • Courses: 2 Hour(s) per week x 14 weeks
  • Project: 1 Hour(s) per week x 14 weeks
  • Lab: 2 Hour(s) per week x 14 weeks
  • Type: optional
  • Semester: Fall
  • Exam form: Oral (winter session)
  • Subject examined: Distributed intelligent systems
  • Courses: 2 Hour(s) per week x 14 weeks
  • Project: 1 Hour(s) per week x 14 weeks
  • Lab: 2 Hour(s) per week x 14 weeks
  • Type: optional
  • Semester: Fall
  • Exam form: Oral (winter session)
  • Subject examined: Distributed intelligent systems
  • Courses: 2 Hour(s) per week x 14 weeks
  • Project: 1 Hour(s) per week x 14 weeks
  • Lab: 2 Hour(s) per week x 14 weeks
  • Type: optional
  • Semester: Fall
  • Exam form: Oral (winter session)
  • Subject examined: Distributed intelligent systems
  • Courses: 2 Hour(s) per week x 14 weeks
  • Project: 1 Hour(s) per week x 14 weeks
  • Lab: 2 Hour(s) per week x 14 weeks
  • Type: optional
  • Semester: Fall
  • Exam form: Oral (winter session)
  • Subject examined: Distributed intelligent systems
  • Courses: 2 Hour(s) per week x 14 weeks
  • Project: 1 Hour(s) per week x 14 weeks
  • Lab: 2 Hour(s) per week x 14 weeks
  • Type: optional
  • Semester: Fall
  • Exam form: Oral (winter session)
  • Subject examined: Distributed intelligent systems
  • Courses: 2 Hour(s) per week x 14 weeks
  • Project: 1 Hour(s) per week x 14 weeks
  • Lab: 2 Hour(s) per week x 14 weeks
  • Type: optional
  • Exam form: Oral (winter session)
  • Subject examined: Distributed intelligent systems
  • Courses: 2 Hour(s) per week x 14 weeks
  • Project: 1 Hour(s) per week x 14 weeks
  • Lab: 2 Hour(s) per week x 14 weeks
  • Type: optional

Reference week

Tuesday, 10h - 12h: Lecture GRC001

Thursday, 9h - 11h: Project, labs, other GRB001
GRC002

Thursday, 11h - 12h: Project, labs, other GRB001
GRC002

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