Artificial Intelligence for Game Developers (Revision 1)
Delivery Mode: Individualized study online
Area of Study: Science
Prerequisite: COMP 390 or professor approval.
Instructor: Larbi Esmahi
Faculty: Faculty of Science and Technology
COMP 452 is not available for challenge.
Artificial Intelligence is widely regarded in the computer games industry as the area where the most advances will be made in the coming decades. As well as equipping students for a career in the rapidly growing game industry, this couse will lead students to gain knowledge and skills in AI techniques that apply to other domains such as business planning and engineering.
The primary focus of this course will be on the use of AI techniques for generating efficient intelligent behavior in games. Additional attention will be given to AI algorithms for improving game play experience.
- Identify tasks that can be tackled using AI techniques.
- Select the appropriate AI technique for the problem under investigation.
- Design and implement efficient and robust AI algorithms for game tasks.
- Develop AI game engines.
- Evaluate performance and test the implemented algorithms.
- Unit 1: Introduction to Game AI.
This unit discusses the kind of AI used in game development, presents the model of game AI, and explains the AI engine structure.
- Unit 2: Movement Algorithms and Steering Behaviour.
The unit presents some kinematic movement algorithms and discusses the problems related with the steering behaviour of objects and some of the used solutions.
- Unit 3: Coordinated Movement and Motor Control.
This unit discusses the concepts related to coordinated movements and motor control mechanisms.
- Unit 4: Pathfinding.
This unit presents the main pathfinding algorithms used in game development (i.e. A*, Dijkstra)
- Unit 5: Advanced path finding.
This unit presents advanced techniques for path finding in complex situations.
- Unit 6: Decision Making and Uncertainty.
This unit presents different models used for implementing decision making in games, including decision trees and state machines. It also discusses the models for implementing knowledge uncertainty such as fuzzy logic and Markov systems.
- Unit 7: Advanced Decision Making Systems.
This unit considers the implementation of advanced decision making behaviour such as goal-oriented behaviour, reasoning, and coordinating.
- Unit 8: Scripting Tools and Action Execution.
The material in this unit discusses the use of scripting languages for implementing AI techniques and how decision actions are executed in the game. It also introduces board games and Minimaxing.
- Unit 9: Introduction to Learning Mechanisms.
This unit covers the basic concepts of learning mechanisms and presents some algorithms for implementing action prediction, decision learning, and reinforcement learning.
To receive credit for COMP 452, you must achieve a course composite grade of at least “D” (50 percent), an average grade of at least 50 percent on the assignments, and a grade of at least 50 percent on the final examination. The weighting of the composite grade is as follows:
To learn more about assignments and examinations, please refer to Athabasca University's online Calendar.
Ian Millington. Artificial Intelligence for Computer Games, second edition. Morgan Koffman, 2009.
Brian Schwab. AI Game Engine Programming. Charles River Media, 2004. (Not included with course materials package.)
The remainder of the learning materials for COMP 452 is distributed in electronic format. At this time, those materials include:
- COMP 452 Study Guide
- Detailed description of the requirements for the individual tutor-marked exercises
- A course evaluation form
- Links to a variety of resources on the web
Additional supporting materials of interest to students of COMP 452 may occasionally be made available electronically.
Special Course Features
COMP 452 is offered only by World Wide Web based computer mediated communications (CMC) mode, and can be completed at the student's workplace or home.
Athabasca University reserves the right to amend course outlines occasionally and without notice. Courses offered by other delivery methods may vary from their individualized-study counterparts.
Revision 1, June 16, 2011
Updated March 09 2018 by SAS