AI Algorithms for Game Design with Python

2h 21mAdvanced2025-03-11

Authors

Eduardo Corpeño

Electrical Engineer, Computer Programmer, and Teacher for 15+ years

Course details

In this intermediate-level course, Eduardo Corpeño explores AI algorithms for game design. Dive into powerful strategies like minimax, alpha-beta pruning, and iterative deepening. Learn how to implement these techniques in Python and enhance your game development skills. Discover the historical context, such as the algorithms used in IBM's Deep Blue, which defeated world chess champion Garry Kasparov. By engaging with real-world examples and hands-on coding exercises, you will develop and strengthen your ability to create intelligent game algorithms. Your learning journey is made seamless with GitHub Codespaces, ensuring no setup hassles. Whether you are a game developer, AI enthusiast, or a programmer looking to sharpen your skills, this course offers in-depth knowledge and practical skills to effectively utilize AI in game design.

Skills covered

Game DevelopmentArtificial Intelligence FoundationsPythonArtificial Intelligence (AI)Programming LanguagesOpen SourceSoftware DevelopmentOne-Off

Concepts

0. Introduction

01 - Playing against a computer is only fun when it's challenging
02 - What you should know
03 - Using the exercise files in GitHub Codespaces

1. Turn-Based Games

04 - Some history as motivation
05 - Different types of games
06 - Tree-based decision-making
07 - Time complexity of brute-force approaches
08 - Time complexity of chess
09 - The cat trap game
10 - The Python setting for the cat trap
11 - Code example - A random cat

2. The Minimax Algorithm

12 - Minimax overview
13 - Minimax example
14 - The minimax algorithm
15 - A word on complexity
16 - Challenge - A perfect cat in a small world
17 - Solution - A perfect cat in a small world
18 - Alpha-beta pruning
19 - The alpha-beta search algorithm
20 - Challenge - A pruning cat
21 - Solution - A pruning cat

3. Depth-Limited Search

22 - Depth-limited search
23 - Writing good evaluation functions
24 - Is alpha-beta pruning still relevant
25 - Challenge - A depth-limited cat
26 - Solution - A depth-limited cat
27 - Challenge - Write your own evaluation function
28 - Solution - Write your own evaluation function

4. Iterative Deepening

29 - The iterative deepening technique
30 - Is iterative deepening a waste of time
31 - Challenge - An iteratively deepening cat
32 - Solution - An iteratively deepening cat
33 - Is iterative deepening really that good
34 - Is alpha-beta pruning really that good

5. Fun with Optimizations

35 - The negamax algorithm
36 - Transposition tables
37 - Monte Carlo evaluation functions

Conclusion

38 - Next steps