Computer Science (COMP) 452

Overview

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.

Course Outline

• 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.

Learning Outcomes

Upon successful completion of this course, you should be able to

• 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.

Evaluation

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.