EPN-V2

ACIT4610 Evolutionary artificial intelligence and robotics Course description

Course name in Norwegian
Evolutionary artificial intelligence and robotics
Weight
10.0 ECTS
Year of study
2020/2021
Course history
Curriculum
FALL 2020
Schedule
  • Introduction

    This course will present complex systems (cellular automata, networks, and agent-based) modelling and programming through state-of-the-art artificial intelligence methods that take inspiration from biology (sub-symbolic and bio-inspired AI methods), such as evolutionary algorithms, neuro-evolution, artificial development, swarm intelligence, evolutionary and swarm robotics.

  • Required preliminary courses

    No formal requirements over and above the admission requirements.

  • Learning outcomes

    On successful completion of the course, students will get both theoretical and practical experience within complex systems and bio-inspired/sub-symbolic AI methods. In particular, students should have the following outcomes defined in terms of knowledge, skills and general competence:

    Knowledge

    The student:

    • Has a deep understanding of complex systems modelling and analysis
    • Has advanced knowledge in sub-symbolic and bio-inspired AI methods
    • Has a clear understanding of key concepts in AI such as emergence, adaptation, evolution.
    • Can relate concepts in artificial intelligence and biological intelligence

    Skills

    The student:

    • Can model and analyse complex systems using cellular automata, networks and agent- based models
    • Can program complex systems using bio-inspired AI methods
    • Can design and implement evolutionary and swarm robotic systems

    General Competence

    The student:

    • Has theoretical and practical understanding of complex and biologically-inspired AI methods and evolutionary robotics methods
    • Can understand and discuss relevance, strength and limitations of complex and biologically inspired systems
    • Is able to work in relevant research projects
  • Teaching and learning methods

    The course consists of lectures and seminars on techniques and methods, as well as a project to be carried out in groups. The project will be chosen from a portfolio of available problems. The students will work in groups and will submit the code and a project report.

    Practical training

    Lab sessions.

  • Course requirements

    None.

  • Assessment

    The assessment will be based on a portfolio of the following:

    • A group project delivery, consisting of a report (7500-3000 words) and code
    • An individual oral examination

    The weight of the two parts is 50 % each.

    The project report should be between 7500-3000 words. Both the code/program and the report will be evaluated. The comprehensiveness of the code/program is evaluated with the assumption that each student in the group has committed about 60 hours towards developing the solution. As a general guideline, the code/program carries a stronger weight than the report.

    The portfolio will be assessed as a whole and the exam cannot be appealed.

  • Permitted exam materials and equipment

    None.

  • Grading scale

    For the final assessment a grading scale from A to E is used, where A denotes the highest and E the lowest pass grade, and F denotes a fail.

  • Examiners

    Two internal examiners. External examiner is used periodically.