Programplaner og emneplaner - Student
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
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- Curriculum
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FALL 2020
- Schedule
- Programme description
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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.
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Required preliminary courses
No formal requirements over and above the admission requirements.
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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
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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.
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Course requirements
None.
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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.
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Permitted exam materials and equipment
None.
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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.
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Examiners
Two internal examiners. External examiner is used periodically.