Contents
This course focuses on the creation of artificial players for computer games. We will focus especially on games for which a forward model can be created and thus a search-based method of artificial intelligence can be used. We will also be dealing with navigation and path-finding (previously covered by NAIL068), though we will not deal with neural networks and evolutionary algorithms (as they are taught elsewhere). We will build on the topics from Artificial Intelligence I (NAIL069) by discussing search-based methods suitable for games, e.g. Monte Carlo Tree Search.
News
Follow the appropriate channel at Gamedev Discord!
https://discord.gg/c49DHBJ
Dates
Lectures + Labs: Thursday 12:20 (S4), Friday 9:00 (S5), first lecture: 2.10.2025
Course Exam
There will be an oral examination done during the examination period. The list of topics will be provided by the end of the semester.
Schedule
| Week | Date | Topic | Lecturer | Content | Materials |
| 1. | 2.10.2025 12:20 |
Course introduction Lecture |
David Šosvald | Introduction to the course content, small lecture about the complexity of creating an artificial player. | TBA |
| 1. | 3.10.2025 9:00 |
Cancelled | |||
| 2. | 9.10.2025 12:20 |
Basics of AI player modeling, Forward model, A*-based agent Lecture |
David Šosvald | AI player and its position in the code architecture. Common intelligent agents models (reflex and goal based) and their correspondence to the game’s architecture. Game models for lookahead, their types. Forward model and game simulation. Example of an A*-based agent construction using Super Mario AI framework and agents developed at MFF. Game space pruning tricks. Details on A* algorithm: Artificial Intelligence I (MFF), Wikipedia |
TBA |
| 2. | 10.10.2025 9:00 |
Pac-Man Agent Lab |
David Šosvald | Pac-Man homework Solve the famous Pac-Man game using A* with a forward model! Delivery deadline: TBA Deliver to: David Šosvald |
TBA |
| 3. | 16.10.2025 12:20 | F.E.A.R. AI, Classical Planning Lecture |
David Šosvald | Let’s dive deep into the way how AI for the game F.E.A.R. was made and is still deemed to be awesome even in contemporary days. | TBA |
| 3. | 17.10.2025 9:00 |
Game AI design crash course Lab |
Peter Guba | A look at solutions to interesting AI challenges in various games. | TBA |
| 4. | 23.10.2025 12:20 | Local navigation Lecture |
Adam Dingle | Local navigation for AI agents: Reynolds steerings, combined behaviors. Context steering. | TBA |
| 4. | 24.10.2025 9:00 | MetaCentrum Lab |
David Šosvald | MetaCentrum homework Introduction to grid computing and hyperparameter testing. Jobs submission, tracking, logging, and results visualisation. TBA: Code from lecture + Homework Delivery deadline: TBA Deliver to: David Šosvald |
|
| 5. | 30.10.2025 12:20 |
Velocity obstacles, Grid-based pathfinding Lecture |
Adam Dingle | Velocity obstacles, reciprocal velocity obstacles. Grid-based pathfinding: JPS, JPS+. | TBA |
| 5. | 31.10.2025 9:00 |
Game AI design crash course Lab |
Peter Guba | A look at solutions to interesting AI challenges in various games. | TBA |
| 6. | 6.11.2025 12:20 |
Spatial awareness, Continuous pathfinding Lecture |
Adam Dingle | BSP trees, raycasting. Visibility graphs. Waypoint graphs. Navigation meshes, funnel algorithm. A* and bidirectional A*. The group assignment will be introduced. | TBA |
| 6. | 7.11.2025 9:00 |
Path-finding Lab |
Adam Dingle | Navigation/pathfinding homework Write an agent that steers a spaceship to gather gems in a series of mazes of increasing difficulty. I will publish the top student scores – will you be among them? |
|
| 7. | 13.11.2025 12:20 |
MCTS – Introduction Lecture |
Peter Guba | The Monte Carlo Tree Search (MCTS) algorithm is introduces and we look at some of its theoretical foundations, namely the Monte Carlo method and the Multi-armed Bandit problem. | TBA |
| 7. | 14.11.2025 9:00 |
Game AI design crash course 3 Lab |
Peter Guba | A look at solutions to interesting AI challenges in various games. | TBA |
| 8. | 20.11.2025 12:20 |
MCTS Adjustments Lecture |
Peter Guba | An exploration of ways of adapting MCTS to games where the default version can’t be applied. MCTS homework assignment will be explained. | TBA |
| 8. | 21.11.2025 9:00 |
RTS AI Lab |
Peter Guba | We discuss how to approach the problem of designing AI for RTS games. | TBA |
| 9. | 27.11.2025 12:20 |
RTS AI Lecture |
Peter Guba | We go over the different components of RTS AI and some ways of approaching them. | TBA |
| 9. | 28.11.2025 9:00 |
Generative AI tools for games Lab |
Peter Guba | A look at some modern generative AI tools and how to use them in games. | TBA |
| 10. | 4.12.2025 12:20 |
RL 1 Lecture |
Adam Dingle | Introduction to deep reinforcement learning. Policies and rewards. Temporal difference learning. Deep neural networks. Backpropagation. Optimizers. | |
| 10. | 5.12.2025 9:00 |
Team project consultations Lab |
Everyone | Will be scheduled individually. | |
| 11. | 11.12.2025 12:20 |
RL 2 Lecture |
Adam Dingle | Algorithms for playing video games and strategy games using deep reinforcement learning. Proximal Policy Optimization (PPO). Generalized Advantage Estimation (GAE). AlphaZero. MuZero. | |
| 11. | 12.12.2025 9:00 |
AlphaZero Lab |
Adam Dingle | ||
| 12. | 18.12.2025 12:20 |
Machine learning in games Lecture |
Peter Guba | How AI based on machine learning is being used in games today – both in gameplay and in the background. | TBA |
| 12. | 19.12.2025 9:00 |
Team project consultations Lab |
Everyone | Will be scheduled individually. | |
| 13. | 25.-26.12. 2025 |
Merry Christmas | |||
| 14. | 1.-2.1.2026 | and Happy New Year | |||
| 15. | 8.1.2026 12:20 |
Group project presentations | Students present the results of their group projects. | ||
| 15. | 9.1.2026 9:00 |
Group project presentations | Students present the results of their group projects. |
The Credit
In order to gain the credit you will be required to finish all the assignments handed out during the semester within their deadlines.
Extra Links
Computational Complexity of Games and Puzzles
Acknowledgement
