PA217 Artificial Intelligence for Computer Games

Faculty of Informatics
Spring 2020
Extent and Intensity
2/0/0. 2 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
doc. Mgr. Hana Rudová, Ph.D. (lecturer)
Mgr. Milan Doležal (assistant)
Mgr. David Kuťák (assistant)
Guaranteed by
doc. Mgr. Hana Rudová, Ph.D.
Department of Computer Systems and Communications - Faculty of Informatics
Supplier department: Department of Computer Systems and Communications - Faculty of Informatics
Fri 10:00–11:50 A318; and Fri 22. 5. 8:00–9:50 A320
Base knowledge of Unity required (PV255 very helpful but not required)
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
there are 29 fields of study the course is directly associated with, display
Course objectives
The course provides information about methods from artificial intelligence used for the development of computer games. Students will learn about data structures and algorithms from artificial intelligence needed for movement, pathfinding, decision making for a single character, strategy and tactics. Students will have practical experience with AI programming.
Learning outcomes
The graduate will be able to apply proper algorithms and approaches from artificial intelligence in computer games.
The graduate will be aware of how to implement artificial intelligence algorithms in the game engine.
  • Introduction and history.
  • Movement: kinematic movement algorithms, steering behaviors.
  • Search and pathfinding: the introduction to search algorithms, A* data structures and heuristics, Monte Carlo search, world representation, hierarchical pathfinding.
  • Decision making for a single character: decision trees, state machines, behavior trees.
  • Strategy and Tactics: waypoints, tactical analyses, coordinated action.
  • Implementation platforms, AI programming in Unity.
  • Millington, I. Artificial intelligence for games. CRC Press, 3rd edition, 2019.
  • Aversa, D., Kyaw, A. S., Peters, C., Unity Artificial Intelligence Programming. Packt Publishing, 4th edition, 2018.
  • Yannakakis, G. N., Togelius, J., Artificial Intelligence and Games. Springer, 2018.
  • Buckland, M., Programming Game AI by Example, Jones & Bartlett Learning, 2004.
Teaching methods
Standard lecture, no drills, two homeworks including AI programming in Unity. Lectures include exercises and programming examples.
Assessment methods
Evaluation is completed based on the distance oral examination (80 points) and two homeworks with practical examples solved during the semester (10 points per each homework). For each class videoconference, 1 point may be given for asking the questions about past classes; up to 2 points may be given for responding to the questions. Successful completion of the course requires getting 40 points for the distance oral examination at least and 8 points for homeworks at least. Evaluation is A more than 90, B 89-80, C 79-70, D 69-60, E 59-50.
Language of instruction
Further Comments
Study Materials
The course is taught annually.
Teacher's information
The course is also listed under the following terms Spring 2021.
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