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AI Algorithms for Gaming

via LinkedIn Learning

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Explore some of the most popular AI algorithms used to create two-player, turn-based games that are challenging enough to keep players guessing.

Syllabus

Introduction

Playing against a computer is only fun when it's challenging
What you should know

1. Turn-Based Games

Some history as motivation
Different types of games
Tree-based decision-making
Time complexity of brute force approaches
Time complexity of chess
The cat trap game
The Python setting for the cat trap
Code example: A random cat

2. The Minimax Algorithm

Minimax overview
Minimax example
The minimax algorithm
A word on complexity
Code example: A perfect cat in a small world
Alpha-beta pruning
The alpha-beta search algorithm
Code example: A pruning cat

3. Depth-Limited Search

Depth-limited search
Writing good evaluation functions
Is alpha-beta pruning still relevant?
Challenge: Write your own evaluation function
Challenge solution
Code example: A depth-limited cat

4. Iterative Deepening

The iterative deepening technique
Is iterative deepening a waste of time?
Code example: An iteratively deepening cat
Is iterative deepening really that good?
Is alpha-beta pruning really that good?

5. Fun with Optimizations

The negamax algorithm
Transposition tables
Monte Carlo evaluation functions

Conclusion

Next steps

Taught by

Eduardo Corpeño

Reviews

4.6 rating at LinkedIn Learning based on 150 ratings

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