Preliminary public information for, subject to change:
Preliminary info last published 18/03-19

Artificial Life & Evolutionary Robotics: Theory, Methods and Art

Course info
Language:
English
ECTS points:
7.5
Course code:
KGARLER1KU
Offered to guest students:
yes
Offered as a single subject:
yes
Price (single subject):
10625 DKK (incl. vat)
Programme
Level:
MSc. Master
Programme:
Master of Science in Information Technology (Games)
Staff
Course semester
Semester
Forår 2020
Start
27 January 2020
End
31 August 2020
Abbreviation
20201
Exam
Abstract

The goal of the course is to teach the students an understanding of the foundation and philosophical basis of artificial life, its methods, and their practical use in games, robotics, and art.

Description

This interdisciplinary course connects topic from robotics, philosophical considerations, and collective intelligence. It covers cutting edge research and techniques for building more life-like machines, interfaces, and algorithms.

Students will understand the technical and theoretical foundations and philosophical basis of artificial life, its methods, and their practical use in games, robotics, and art.

This interdisciplinary course is open across study-lines and disciplines.

The course will partially cover the following list of topics:

  • Philosophical perspectives on the nature of life and the possibility of artificial life
  • Soft artificial life (Cellular automata, Artificial Evolution, Neutral Networks, Neuroevolution, NEAT / HyperNEAT, Generative and Developmental Systems, CPPNs, HyperNEAT, L-Systems)
  • Hard artificial life (Artificial life robots, Evolutionary robotics, the reality gap, Co-evolution bodies and brains)
  • Collective Intelligence (Swarm robotics, Evolution of communication, Cellular robot systems, Self-organizing and self-reproducing systems)
  • Wet artificial life (Artificial chemical life)
  • The use of artificial life in design and art
Intended learning outcomes

After the course, the student should be able to:

  • Describe and theorize on artificial life algorithms.
  • Identify tasks that can be tackled through advanced bio-inspired techniques and select the appropriate technique for the problem under investigation.
  • Compare the performance of different alife techniques and reflect on their suitability for different tasks.
  • Design and implement efficient and robust advanced alife algorithms.
  • Evaluate the algorithms in simulated or physical environment.
Ordinary exam
Exam type:
D: Written report with oral defence, external (7-trinsskala)
Exam variation:
D22: Submission of written work with following oral exam supplemented by the work submitted. The oral exam will be supplemented by the submitted work, i.e. the submitted work supplements a fixed syllabus from the course base.