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MLKI2000 Machine Learning and Artificial Intelligence in Games

Learning outcomes

By completing the course, the student will have achieved the following learning outcomes: 

Knowledge 

The student 

  • understands the complexity of various machine learning (ML) algorithms and their limitations in training
  • understands issues and roles of Artificial Intelligence (AI) in the design of games 
  • understands tactical and strategic AI used in gaming scenario 

Skills 

The student 

  • can explain the general idea behind ML, as well as specific algorithms that are being used in real world scenarios 
  • can use ML methodology for research and industrial settings using current trend of ML software libraries 
  • can implement and apply ML methods to any chosen game 
  • can produce game that the avatars are navigate around and served with the purpose based on goal-oriented/utility-theory 
  • is capable of confidently applying common ML algorithms in practice and implementing their own algorithm
  • can perform experiments in ML using real-world game scenario
  • can program autonomous movement of avatars 
  • can design and implement decision making and coordinating action based on heuristic, fuzzy sets or logics
  • can read and understand scientific publications on ML/AI and formulate current issues, choice of methods, and results in a short, concise manner 

General competence 

The student 

  • is able to plan and carry out varied tasks in accordance with ethical requirements and guidelines 
  • is familiar with relevant issues of professional ethics, and is able to make a contribution to a professional community

Course content

Central topics: 

  • Artificial Neural Network 
  • Q-learning Reinforcement Learning 
  • Use of popular Machine Learning libraries
  • Goal-Oriented Action Planning 
  • Utility-based Theory  

Teaching and working methods

The course is organised as a combination of lectures, practical exercises and supervision. 

Coursework requirements

  • 3–5 individual assignments for ML and AI respectively 
  • attendance at lectures in accordance with the teaching plan  
  • attendance at laboratory teaching in accordance with the teaching plan 

Examination

  • 1 individual folder assessment on Machine Learning which counts for 50% the final grade 
  • 1 individual folder assessment on Artificial Intelligence which counts for 50% the final grade 

To pass the course, both examinations must receive a passing grade. 

Alphabetical grades are used, on a scale from A to F, with E as the lowest passing grade.