Course description forACIT4610 Evolutionary artificial intelligence and robotics

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 project delivery, consisting of a report (about 15 to 30 pages) and code
  • An individual oral examination

The weight of the two parts is about 50% each.

The project report should be between 15 and 30 pages. 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 oral 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.

Course information

Course name in Norwegian
Evolutionary artificial intelligence and robotics
Study programme
Fall: Master's Degree Programme in Applied Computer and Information Technology
Weight
10 ECTS
Year of study
2019
Schedule
FALL 2019
Programme description
Fall 2019: Master's Degree Programme in Applied Computer and Information Technology
Subject History