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AI Algorithms for Game Design with Python

via LinkedIn Learning

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Overview

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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

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