Implementing AI to Play Games [Video]

Preview in Mapt

Implementing AI to Play Games [Video]

Devangini Patel

Harness the power of AI to solve and play powerful and smarter puzzles and games by itself and against humans!

Quick links: > What will you learn?> Table of content

Mapt Subscription
FREE
$29.99/m after trial
Video
$106.25
RRP $124.99
Save 14%
What do I get with a Mapt Pro subscription?
  • Unlimited access to all Packt’s 5,000+ eBooks and Videos
  • Early Access content, Progress Tracking, and Assessments
  • 1 Free eBook or Video to download and keep every month after trial
What do I get with an eBook?
  • Download this book in EPUB, PDF, MOBI formats
  • DRM FREE - read and interact with your content when you want, where you want, and how you want
  • Access this title in the Mapt reader
What do I get with Print & eBook?
  • Get a paperback copy of the book delivered to you
  • Download this book in EPUB, PDF, MOBI formats
  • DRM FREE - read and interact with your content when you want, where you want, and how you want
  • Access this title in the Mapt reader
What do I get with a Video?
  • Download this Video course in MP4 format
  • DRM FREE - read and interact with your content when you want, where you want, and how you want
  • Access this title in the Mapt reader
$0.00
$106.25
$29.99 p/m after trial
RRP $124.99
Subscription
Video
Start 14 Day Trial

Frequently bought together


Implementing AI to Play Games [Video] Book Cover
Implementing AI to Play Games [Video]
$ 124.99
$ 106.25
Graph Algorithms for AI in Games [Video] Book Cover
Graph Algorithms for AI in Games [Video]
$ 124.99
$ 106.25
Buy 2 for $35.00
Save $214.98
Add to Cart

Video Details

ISBN 139781788476539
Course Length3 hours and 23 minutes

Video Description

In video games, Artificial Intelligence is used to generate responsive or intelligent behavior primarily in Non-Player Characters (NPCs), like human intelligence. In this course, we look at games; we understand how to decide which move to take based on future possibilities and payoffs (just as, in chess, we look n-moves ahead into the future).

We explore how to solve applications where there are a number of parameters to optimize, such as time or distance, and the possibilities are exponential. We look at how to design the various stage of the evolutionary algorithm that will control performance. We take a sample game—Tic-Tac-Toe—and show how various steps of the algorithm are implemented in code. And we look at color filling as a constraint satisfaction application and see how various algorithm concepts are applied in code.

Finally, we also explain a trip-planning application and see how the application is solved through evolutionary algorithms.

Style and Approach

A fun course packed with step-by-step instructions, working examples, and helpful advice.

You will learn how AI is used to make your games smarter. This comprehensive course is divided into clear bite-size chunks so you can learn at your own pace and focus on the areas of most interest to you.

Table of Contents

Constraint Satisfaction Problem
The Course Overview
Installing Python and Its Libraries
Recap of DFS
Introduction to Coloring Application
Constraint Satisfaction Problem Formulation
Constraint Satisfaction Problem Formulation (Continued)
DFS to Backtracking Search
Using Heuristics in Backtracking Search
Forward Checking
Arc Consistency
Using AI to Play Games
Introduction to Tic-Tac-Toe Game
Understanding a Game Move
Game Search Formulation
Score Evaluation
Depth Limited Search
Minimax Search
Tic-Tac-Toe Game
Alpha-Beta Pruning
Evolutionary Search
Introduction to N Queens Puzzle
Population
Fitness and Selection
Variation Operations
Termination
DIY
Comparison with Other Methods

What You Will Learn

  • Perform searches in games
  • Implement a game evaluation function in your game
  • Quantize the desirability of a move for your game
  • Explore a game tree using AI
  • Work on how to optimize a search
  • Design an evolutionary algorithm
  • Implement various stages of the evolutionary algorithm
  • Improve the performance of evolutionary algorithms by adding visualizations
  • How to solve a search which has certain constraints for the variables

Authors

Table of Contents

Constraint Satisfaction Problem
The Course Overview
Installing Python and Its Libraries
Recap of DFS
Introduction to Coloring Application
Constraint Satisfaction Problem Formulation
Constraint Satisfaction Problem Formulation (Continued)
DFS to Backtracking Search
Using Heuristics in Backtracking Search
Forward Checking
Arc Consistency
Using AI to Play Games
Introduction to Tic-Tac-Toe Game
Understanding a Game Move
Game Search Formulation
Score Evaluation
Depth Limited Search
Minimax Search
Tic-Tac-Toe Game
Alpha-Beta Pruning
Evolutionary Search
Introduction to N Queens Puzzle
Population
Fitness and Selection
Variation Operations
Termination
DIY
Comparison with Other Methods

Video Details

ISBN 139781788476539
Course Length3 hours and 23 minutes
Read More

Read More Reviews

Recommended for You

Graph Algorithms for AI in Games [Video] Book Cover
Graph Algorithms for AI in Games [Video]
$ 124.99
$ 106.25
LaTeX A-Z: from beginner to advanced in less than 3 hours [Video] Book Cover
LaTeX A-Z: from beginner to advanced in less than 3 hours [Video]
$ 94.99
$ 80.75
How To Program Your Own Breakout Game using Visual C# [Video] Book Cover
How To Program Your Own Breakout Game using Visual C# [Video]
$ 73.99
$ 62.90
From 0 to 1: Hive for Processing Big Data [Video] Book Cover
From 0 to 1: Hive for Processing Big Data [Video]
$ 49.99
$ 42.50
Introduction to Rust Programming [Video] Book Cover
Introduction to Rust Programming [Video]
$ 124.99
$ 106.25
Introduction to JVM Languages – Clojure, Kotlin, and Groovy [Video] Book Cover
Introduction to JVM Languages – Clojure, Kotlin, and Groovy [Video]
$ 124.99
$ 106.25