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SPIS2001 Machine Learning and Artificial Intelligence in Games

    • Number of credits
      10
    • Teaching semester
      2027 Spring
    • Language of instruction
      English
    • Campus
      Hamar
    • Required prerequisite knowledge

      Recommended: 2VSIM101-Visualisation and Simulation

Course content
  • Artificial Neural Network
  • Q-learning Reinforcement Learning
  • Genetic Algorithm Learning
  • Waypoint Navigations
  • Behaviour Tree

Learning Outcome

Once the student has passed the course, they 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 perform experiments in ML using real-world game scenario
  • is capable of confidently applying common ML algorithms in practice and implementing their own algorithm
  • can program autonomous movement of avatars
  • can read and understand scientific publications on ML/AI and formulate current issues, choice of methods, and results in a short, concise manner
  • capable of producing games where the avatar navigates around and performs actions based on waypoint, behaviour tree
  • can design game to have the avatar to learn about the playing scenario based on genetic algorithm and artificial neural network

 

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
Teaching and working methods

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

Required coursework
  • 3–5 individual assignments for ML and AI respectively
  • participation in teaching activities in accordance with the teaching plan
Form of assessment
  • 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.

Assessments
Form of assessmentGrading scaleGroupingDuration of assessmentSupport materialsProportionComment
Portfolio Assessment
ECTS - A-F
Individual
  • All
50
Portfolio Assessment
ECTS - A-F
Individual
  • All
50
Faculty
Faculty of Audiovisual Media and Creative Technologies
Department
Department of Game Development - The Game School