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Kies de Nederlandse taal
Course module: INFOMAIGT
INFOMAIGT
AI for game technology
Course infoSchedule
Course codeINFOMAIGT
ECTS Credits7.5
Category / LevelM (M (Master))
Course typeCourse
Language of instructionEnglish
Offered byFaculty of Science; Graduate School of Natural Sciences; Graduate School of Natural Sciences;
Contact persondr. T. Miltzow
E-mailT.Miltzow@uu.nl
Lecturers
Course contact
dr. T. Miltzow
Other courses by this lecturer
Lecturer
dr. T. Miltzow
Other courses by this lecturer
Lecturer
J.L. Vermeulen, MSc
Other courses by this lecturer
Teaching period
1-GS-4  (06/09/2021 to 08/07/2022)
Teaching period in which the course begins
1-GS
Time slotC: C (MON-afternoon, TUE-aftern,THU-morn)
Study mode
Full-time
Enrolment periodfrom 31/05/2021 up to and including 27/06/2021
Course application processOsiris Student
Enrolling through OSIRISYes
Enrolment open to students taking subsidiary coursesYes
Pre-enrolmentNo
Post-registrationYes
Post-registration openfrom 23/08/2021 up to and including 20/09/2021
Waiting listYes
Course placement processadministratie onderwijsinstituut
Course goals
After completing the course, the student
  • will understand that the current level of Artificial Intelligence in computer games is very low
  • will understand that adding Artificial Intelligence to game characters is not always useful. The student
  • will have learned in which cases Artificial Intelligence techniques can help to achieve the goal of any game: more attractive game play.
  • will have experienced that adding Artificial Intelligence to a computer game can be hard in practice.
  • will understand the difficulties of applying Artificial Intelligence techniques in concrete computer games.
  • will know which Artificial Intelligence techniques are suited for application in the computer game environments and which are not.
Content
In this course the use of AI techniques in games is explored, for instance in serious gaming and training. Distributing game control over several independently operating agents is discussed, several path-planning techniques useful for computer games are investigated, and dynamic re-planning algorithms useful for dealing with dynamic environments are described. Furthermore, machine learning techniques such as evolutionary algorithms with neural networks are discussed, as well as some techniques and solutions for multi-agent cooperation.