Syllabus
Spring Semester, 2026
Classroom: Langdale Hall Room 301
Time: 05:30 PM - 07:15 PM
CRN: 17592
Instructor: Dr. Michael Weeks
Computer Science Department
This syllabus provides a general plan; deviations may be necessary.
Office: 25 Park Place, room 754
Office Hours: 4-5 T, Th
web-page: http://hallertau.cs.gsu.edu/~mweeks
Teaching Assistant:
Tyree McCloud
tmccloud6 @ student.gsu.edu
TA's Office hours:
2:30 - 4:30 Monday and Wednesday
(Webex)
Click here for the Syllabus policies
FINAL EXAM
The Final Exam will be presentations, held on
Tuesday, May 5th, 2026, 16:15-18:45
(the official final exam time).
See the registrar's website.
DESCRIPTION
Advanced AI algorithms and tools used in gaming; topics include
genetic algorithms and neuroevolution, Monte-Carlo tree search,
finite state machines,
procedural content generation,
path finding, agents, and
reinforcement learning.
TEXTS
- Selected research papers.
PREREQUISITES
CSc 3320 or consent of instructor.
Programming maturity is assumed.
In addition, students are expected to know
discrete structures applicable to computer science, number bases,
logic, sets, Boolean algebra, graph theory.
CONTENT
This course discusses research papers related to AI in games,
including
genetic algorithms and neuroevolution, Monte-Carlo tree search,
finite state machines,
procedural content generation,
path finding, agents, and
reinforcement learning.
Students will read research papers, present them, answer questions about them, and review each other's presentations.
GRADING
- Participation and Attendance (and paying attention) will constitute 5% of the course grade.
- Any pop-quizzes will factor into the Participation grade.
- Approximately 4 Assignments will constitute 40% of the course grade. (There will be at least 3 assignments, maybe as many as 6). Assignments will include a paper summary, and a literature review. These may be in several parts or forms, such as requiring a written paper, an in-class presentation, a question-answer session, and potentially a video.
- The project will constitute 55% of the course grade. This includes several reports and/or in-class presentations (e.g. a project abstract, a mid-semester update, and a final presentation).
| Points | Deliverable | weights | category |
| 100 pts | Written Paper Summary | 1 | assignment |
| 100 pts | Paper Summary Presentation | 1 | assignment |
| 100 pts | Feedback (on others' Paper Summary Presentations) | 1 | assignment |
| 10 pts | Project Abstract | 1 | project |
| 100 pts | Project Update Video | 1 | project |
| 100 pts | Feedback (on others' Project Update Videos) | 1 | project |
| 10 pts | Milestone Checklist 1 | 1 | project |
| 100 pts | Written Literature Review | 1 | assignment |
| 100 pts | Literature Review Presentation | 1 | assignment |
| 100 pts | Feedback (on others' Literature Review Presentations) | 1 | assignment |
| 10 pts | Milestone Checklist 2 | 1 | project |
| 100 pts | Final Project Video | 1 | project |
| 100 pts | Feedback (on others' Final Project Videos) | 1 | project |
| 100 pts | Feedback about yourself/your group | 1 | project |
| 100 pts | Final Project files ("README.txt", "Design", "Code", "Assets", etc.) | 1 | project |
LEARNING OUTCOMES
By the end of this course, students will know:
Fundamental Concepts
- Analyze and explain the role of AI in video games, including its history and impact on game design and player experience.
- Understand and apply foundational AI concepts such as state-space representation, heuristic search, and decision-making processes.
- Implement and evaluate various pathfinding algorithms like A*, Dijkstra's, and Breadth-First Search to enable non-player characters (NPCs) to navigate game environments efficiently.
- Develop and critique steering behaviors (e.g., seek, flee, flocking) for realistic and dynamic character movement.
- Design and create finite-state machines (FSMs) and behavior trees to model complex NPC behaviors, from simple patrols to intricate combat strategies.
- Utilize utility-based AI to enable NPCs to make intelligent, context-aware decisions in dynamic situations.
- Apply and evaluate machine learning techniques, such as Q-learning and neural networks, to enable NPCs to learn from player interactions and adapt their behaviors.
- Train a simple game AI to play a classic game (e.g., Tic-Tac-Toe, Pac-Man) using reinforcement learning.
- Develop and implement systems to model player behavior and preferences, allowing for dynamic difficulty adjustment and personalized content.
- Create procedural content, such as generating levels, quests, or textures, using AI algorithms.
- Integrate multiple AI algorithms to create a complete and compelling AI for a game prototype.
- Debug and optimize AI code to ensure efficient and performant behavior within a game engine.
- Communicate and present their AI design and implementation choices effectively.