Learning Machines

2019-2020

Course Objective

Objectives:
1) To understand the difference between machine learning and learning
machines. (Knowledge and understanding)
2) To equip a given robot with learning abilities and experimentally
test the resulting performance. (Apply knowledge and understanding)
3) To experience the difference between simulated and real robots (to
this end, we use Thymio robots enhanced with Raspberry Pi 'brains',
cameras, and powerful batteries. For the simulations we use our own
software that
allows easy portability of code between simulated and real Thymios)
(Apply knowledge and understanding) (Make judgements)

Course Content

This course concerns robots that can adjust and improve their behaviour
over time.

The course has a strong hands-on flavour. After two introductory
lectures students have to develop and implement the learning method of
their choice in simulation. In particular, adequate robot controllers
have to be learned autonomously for two tasks, maze navigation and food
collection. After testing and tuning the methods in simulation, the best
learned robot controller must be ported to a real Thymio and the real
world performance compared with that observed in simulation.

Teaching Methods

The course is short and requires intensive work. Students will work in
teams of three, attending two obligatory workshops each week. During the
last lecture, we will have a live demonstration of all robots.

Method of Assessment

Each student team will have one robot that has to learn to solve three
tasks (one per week).

Grading is based on:
1) task performance of the robots:
a. Obstacle avoidance: the robot should move around without hitting any
obstacles. (25%)
b. Foraging: the robot should search for 'food', approach it and touch
it. (25%)
c. Prey catching: the robot (predator) should catch another robot (prey)
moving around in the arena. (25%)
2) short report of the approach and experimental results (25%)

Entry Requirements

Prerequisites: Python.

Target Audience

MSc Artificial Intelligence

General Information

Course Code XM_0061
Credits 6 EC
Period P3
Course Level 500
Language of Tuition English
Faculty Faculty of Science
Course Coordinator prof. dr. A.E. Eiben
Examiner prof. dr. A.E. Eiben
Teaching Staff prof. dr. A.E. Eiben

Practical Information

You need to register for this course yourself

Last-minute registration is available for this course.

Teaching Methods Seminar, Lecture