Modern Artificial Intelligence
AbstractThe goal of the course is to teach the understanding, design, implementation and use of modern artificial intelligence (AI) and computational intelligence (CI) techniques for generating efficient intelligent behaviours in games and other simulation environments. Additional focus will be given to state-of-the-art AI algorithms for improving gameplay experience.
Modern artificial intelligence and computational intelligence have many applications inside and outside computer games. The techniques taught in this course are applicable to games, simulation environments, robotics, and many other areas.
Students learn a broad understanding of the theoretical, practical and implementation side of AI algorithms.
The course will partly cover the following topics (AI techniques and problems):
- AI techniques
- Finite-state machines
- Behaviour trees
- Evolutionary algorithms
- Artificial neural networks
- Deep Learning
- Reinforcement learning
- Hybrid approaches
- Non-player character AI
- Adaptation and learning (off-line and on-line)
- Player behaviour modelling
- Player experience modelling General video game playing
- Dynamic difficulty adjustment
Formal prerequisitesStudents must have completed a course on programming such as "Introductory Programming", or "Object-Oriented Programming". Having completed the "Making Games" course is a plus.
Moreover the student must always meet the admission requirements of the IT University.
Intended learning outcomes
After the course, the student should be able to:
- Theorize about and describe the AI algorithms covered in the class.
- Identify tasks that can be tackled through advanced AI techniques and select the appropriate technique for the problem under investigation.
- Compare the performance of different AI techniques and reflect on their suitability for different domains
- Design and implement efficient and robust advanced AI algorithms.
- Work efficiently in groups
Ordinary examExam type:
D: Submission of written work with following oral, External (7-point scale)
D2G: Submission for groups with following oral exam supplemented by the submission. Shared responsibility for the report.