Coursera Plus Annual is 40% Off — Ends July 13.

Class Central is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

LinkedIn Learning

AI Algorithms for Game Design with Python

via LinkedIn Learning

Write review
TrainSec Ad

Master Windows Internals - Kernel Programming, Debugging & Architecture

Learn More → DataCamp Ad

Power BI Fundamentals - Create visualizations and dashboards from scratch

Learn More →

Overview

Google, IBM & Meta Certificates – 40% Off
One plan covers every Professional Certificate on Coursera.
Unlock All Certificates
Learn aboutAI algorithms tailored to game design, including minimax, alpha-beta pruning, and iterative deepening, and get hands-on experience implementing them in Python.

Syllabus

Introduction
  • Playing against a computer is only fun when it's challenging
  • What you should know
  • Using the exercise files in GitHub Codespaces
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
  • Challenge: A perfect cat in a small world
  • Solution: A perfect cat in a small world
  • Alpha-beta pruning
  • The alpha-beta search algorithm
  • Challenge: A pruning cat
  • Solution: A pruning cat
3. Depth-Limited Search
  • Depth-limited search
  • Writing good evaluation functions
  • Is alpha-beta pruning still relevant?
  • Challenge: A depth-limited cat
  • Solution: A depth-limited cat
  • Challenge: Write your own evaluation function
  • Solution: Write your own evaluation function
4. Iterative Deepening
  • The iterative deepening technique
  • Is iterative deepening a waste of time?
  • Challenge: An iteratively deepening cat
  • Solution: 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

4.8 rating at LinkedIn Learning based on 4 ratings

Start your review of AI Algorithms for Game Design with Python

Never Stop Learning.

Get personalized course recommendations, track subjects and courses with reminders, and more.

Sign up for free
Someone learning on their laptop while sitting on the floor.