Unity Artificial Intelligence Programming: Add powerful, believable, and fun AI entities in your game with the power of Unity, 5th Edition

✍ Scribed by Dr. Davide Aversa

• Publisher: Packt Publishing
• Year: 2022
• Tongue: English
• Leaves: 309
• Edition: 5
• Category: Library

✦ Synopsis

Learn and implement game AI in Unity to build smart environments and enemies with A pathfinding, finite state machines, behavior trees, and the NavMesh

Key Features

• Explore the latest Unity features to make AI implementation in your game easier
• Build richer and more dynamic games using AI concepts such as behavior trees and navigation meshes
• Implement character behaviors and simulations using the Unity Machine Learning toolkit

Book Description

Developing artificial intelligence (AI) for game characters in Unity has never been easier. Unity provides game and app developers with a variety of tools to implement AI, from basic techniques to cutting-edge machine learning-powered agents. Leveraging these tools via Unity's API or built-in features allows limitless possibilities when it comes to creating game worlds and characters.

The updated fifth edition of Unity Artificial Intelligence Programming starts by breaking down AI into simple concepts. Using a variety of examples, the book then takes those concepts and walks you through actual implementations designed to highlight key concepts and features related to game AI in Unity. As you progress, you'll learn how to implement a finite state machine (FSM) to determine how your AI behaves, apply probability and randomness to make games less predictable, and implement a basic sensory system. Later, you'll understand how to set up a game map with a navigation mesh, incorporate movement through techniques such as A pathfinding, and provide characters with decision-making abilities using behavior trees.

By the end of this Unity book, you'll have the skills you need to bring together all the concepts and practical lessons you've learned to build an impressive vehicle battle game.

What you will learn

• Understand the basics of AI in game design
• Create smarter game worlds and characters with C# programming
• Apply automated character movement using pathfinding algorithm behaviors
• Implement character decision-making algorithms using behavior trees
• Build believable and highly efficient artificial flocks and crowds
• Create sensory systems for your AI world
• Become well-versed with the basics of procedural content generation
• Explore the application of machine learning in Unity

Who this book is for

This Unity artificial intelligence book is for Unity developers with a basic understanding of C# and the Unity Editor who want to expand their knowledge of AI Unity game development.

Table of Contents

1. Introduction to AI
2. Finite State Machines
3. Randomness and Probability
4. Implementing Sensors
5. Flocking
6. Path Following and Steering Behaviors
7. A* Pathfinding
8. Navigation Mesh
9. Behavior Trees
10. Procedural Content Generation
11. Machine Learning in Unity
12. Putting It All Together

✦ Table of Contents

Cover
Title
Copyright and Credits
Table of Contents
Part 1:Basic AI
Chapter 1: Introduction to AI
Understanding AI
AI in video games
AI techniques for video games
Finite state machines
Randomness and probability in AI
The sensor system
Flocking, swarming, and herding
Path following and steering
A pathfinding
Navigation meshes
Behavior trees
Locomotion
Summary
Chapter 2: Finite State Machines
Technical requirements
Implementing the player's tank
Initializing the Tank object
Shooting the bullet
Controlling the tank
Implementing a Bullet class
Setting up waypoints
Creating the abstract FSM class
Using a simple FSM for the enemy tank AI
The Patrol state
The Chase state
The Attack state
The Dead state
Taking damage
Using an FSM framework
The AdvancedFSM class
The FSMState class
The state classes
The NPCTankController class
Summary
Chapter 3: Randomness and Probability
Technical requirements
Introducing randomness in Unity
Randomness in computer science
The Unity Random class
A simple random dice game
Learning the basics of probability
Independent and correlated events
Conditional probability
Loaded dice
Exploring more examples of probability in games
Character personalities
Perceived randomness
FSM with probability
Dynamically adapting AI skills
Creating a slot machine
A random slot machine
Weighted probability
A near miss
Summary
Further reading
Chapter 4: Implementing Sensors
Technical requirements
Basic sensory systems
Scene setup
The player's tank and the aspect class
The player's tank
Aspect
AI characters
Sense
Sight
Touch
Testing
Summary
Part 2:Movement and Navigation
Chapter 5: Flocking
Technical requirements
Basic flocking behavior
Individual behavior
Controller
Alternative implementation
FlockController
Summary
Chapter 6: Path Following and Steering Behaviors
Chapter 7: A Pathfinding
Technical requirements
Revisiting the A algorithm
Implementing the A algorithm
Node
PriorityQueue
The GridManager class
The AStar class
The TestCode class
Setting up the scene
Testing the pathfinder
Summary
Chapter 8: Navigation Mesh
Technical requirements
Setting up the map
Navigation static
Baking the NavMesh
NavMesh agent
Updating an agent's destinations
Setting up a scene with slopes
Baking navigation areas with different costs
Using Off Mesh Links to connect gaps between areas
Generated Off Mesh Links
Manual Off Mesh Links
Summary
Part 3:Advanced AI
Chapter 9: Behavior Trees
Technical requirements
Introduction to BTs
A simple example – a patrolling robot
Implementing a BT in Unity with Behavior Bricks
Set up the scene
Implement a day/night cycle
Design the enemy behavior
Implementing the nodes
Building the tree
Attach the BT to the enemy
Summary
Further reading
Chapter 10: Procedural Content Generation
Technical requirements
Understanding Procedural Content Generation in games
Kinds of Procedural Content Generation
Implementing a simple goblin name generator
Generating goblin names
Completing the goblin description
Learning how to use Perlin noise
Built-in Unity Perlin noise
Generating random maps and caves
Cellular automata
Implementing a cave generator
Rendering the generated cave
Summary
Further reading
Chapter 11: Machine Learning in Unity
Technical requirements
The Unity Machine Learning Agents Toolkit
Installing the ML-Agents Toolkit
Installing Python and PyTorch on Windows
Installing Python and PyTorch on macOS and Unix-like systems
Using the ML-Agents Toolkit – a basic example
Creating the scene
Implementing the code
Adding the final touches
Testing the learning environment
Training an agent
Summary
Further reading
Chapter 12: Putting It All Together
Technical requirements
Developing the basic game structure
Adding automated navigation
Creating the NavMesh
Setting up the agent
Fixing the GameManager script
Creating decision-making AI with FSM
Summary
Index