Published 7/2022
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz
Language: English | Size: 703.08 MB | Duration: 1h 32m
Implementation of the Minimax algorithm (and Alpha-beta pruning) in Python
What you’ll learn
The minimax algorithm
The implementation of the Minimax algorithm in Python
The Alpha-Beta pruning algorithm
The implementation of the Alpha-Beta pruning algorithm in Python
Artificial intelligence in video games
Improving your Python knowledge through practice
Requirements
Basic programming knowledge
Description
In this artificial intelligence course, we will implement the Minimax algorithm and its optimized version, the Alpha Beta pruning algorithm. We will apply the algorithm to the tic-tac-toe game, creating an artificial intelligence which cannot be beaten. The algorithm will be implemented in a generic way, so that it can be easily applied to other games. This course is aimed at developers who would like to add artificial intelligence into their games, those who would like to implement the Minimax algorithm, as well as students and artificial intelligence enthusiasts. This course also aims to be a stepping stone to more advanced courses in artificial intelligence, machine learning and deep learning.This course, taught using the Python programming language, requires basic programming skills. If you don’t have the required foundation, I recommend getting up to speed by taking a crash course in programming (if you wouldd like, I offer a crash course in Python programming on Udemy). Concepts covered: The Minimax algorithm and its implementation in PythonThe Alpha-beta pruning algorithm and its implementation in PythonArtificial intelligence in video gamesThe creation of artificial intelligence modules and frameworksThe concept of heuristic functionsDo not wait any longer before jumping into the world of artificial intelligence!
Overview
Section 1: Introduction
Lecture 1 Introduction
Section 2: Minimax Algorithm
Lecture 2 Minimax Algorithm
Lecture 3 Pseudocode
Lecture 4 Generic API
Section 3: Implementation
Lecture 5 Minimax
Lecture 6 Game-state
Lecture 7 Tic Tac Toe
Lecture 8 User Interface (UI)
Lecture 9 Tests
Lecture 10 Finishing up
Section 4: Alpha-beta pruning & conclusion
Lecture 11 Alpha-beta pruning
Lecture 12 Conclusion
For those who want to learn the Minimax algorithm,For developers who want to introduce artificial intelligence into their games,For those who are interested in artificial intelligence
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