Book Image

Unity 2018 Artificial Intelligence Cookbook - Second Edition

By : Jorge Palacios
Book Image

Unity 2018 Artificial Intelligence Cookbook - Second Edition

By: Jorge Palacios

Overview of this book

Interactive and engaging games come with intelligent enemies, and this intellectual behavior is combined with a variety of techniques collectively referred to as Artificial Intelligence. Exploring Unity's API, or its built-in features, allows limitless possibilities when it comes to creating your game's worlds and characters. This cookbook covers both essential and niche techniques to help you take your AI programming to the next level. To start with, you’ll quickly run through the essential building blocks of working with an agent, programming movement, and navigation in a game environment, followed by improving your agent's decision-making and coordination mechanisms – all through hands-on examples using easily customizable techniques. You’ll then discover how to emulate the vision and hearing capabilities of your agent for natural and humanlike AI behavior, and later improve the agents with the help of graphs. This book also covers the new navigational mesh with improved AI and pathfinding tools introduced in the Unity 2018 update. You’ll empower your AI with decision-making functions by programming simple board games, such as tic-tac-toe and checkers, and orchestrate agent coordination to get your AIs working together as one. By the end of this book, you’ll have gained expertise in AI programming and developed creative and interactive games.

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Table of Contents (12 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Sections
Get in touch
Free Chapter
1
Behaviors - Intelligent Movement
Behaviors - Intelligent Movement
Introduction
Creating the behaviors template
Pursuing and evading
Adjusting the agent for physics
Arriving and leaving
Facing objects
Wandering around
Following a path
Avoiding agents
Avoiding walls
Blending behaviors by weight
Blending behaviors by priority
Shooting a projectile
Predicting a projectile's landing spot
Targeting a projectile
Creating a jump system
2
Navigation
Navigation
Introduction
Representing the world with grids
Representing the world with points of visibility
Representing the world with a self-made navigation mesh
Finding your way out of a maze with DFS
Finding the shortest path in a grid with BFS
Finding the shortest path with Dijkstra
Finding the best-promising path with A*
Improving A* for memory – IDA*
Planning navigation in several frames – time-sliced search
Smoothing a path
3
Decision Making
Decision Making
Introduction
Choosing through a decision tree
Implementing a finite-state machine
Improving FSMs: hierarchical finite-state machines
Implementing behavior trees
Working with fuzzy logic
Making decisions with goal-oriented behaviors
Implementing a blackboard architecture
Experimenting with Unity's animation state machine
4
The New NavMesh API
The New NavMesh API
Introduction
Setting up the NavMesh building components
Creating and managing NavMesh for multiple types of agents
Creating and updating NavMesh data at runtime
Controlling the lifetime of the NavMesh instance
Connecting multiple instances of NavMesh
Creating dynamic NavMeshes with obstacles
Implementing some behaviors using the NavMesh API
5
Coordination and Tactics
Coordination and Tactics
Introduction
Handling formations
Extending A* for coordination – A*mbush
Analyzing waypoints by height
Analyzing waypoints by cover and visibility
Creating waypoints automatically
Exemplifying waypoints for decision making
Implementing influence maps
Improving influence with map flooding
Improving influence with convolution filters
Building a fighting circle
6
Agent Awareness
Agent Awareness
Introduction
The seeing function using a collider-based system
The hearing function using a collider-based system
The smelling function using a collider-based system
The seeing function using a graph-based system
The hearing function using a graph-based system
The smelling function using a graph-based system
Creating awareness in a stealth game
7
Board Games and Applied Search AI
Board Games and Applied Search AI
Introduction
Working with the game-tree class
Implementing Minimax
Implementing Negamax
Implementing AB Negamax
Implementing NegaScout
Implementing a Tic-Tac-Toe rival
Implementing a Checkers rival
Implementing Rock-Paper-Scissors AI with UCB1
Implementing regret matching
8
Learning Techniques
Learning Techniques
Introduction
Predicting actions with an N-Gram predictor
Improving the predictor – Hierarchical N-Gram
Learning to use Naïve Bayes classifier
Implementing reinforcement learning
Implementing artificial neural networks
9
Procedural Content Generation
Procedural Content Generation
Introduction
Creating mazes with Depth-First Search
Implementing the constructive algorithm for dungeons and islands
Generating landscapes
Using N-Grams for content generation
Generating enemies with the evolutionary algorithm
10
Miscellaneous
Miscellaneous
Introduction
Creating and managing Scriptable Objects
Handling random numbers better
Building an air-hockey rival
Implementing an architecture for racing games
Managing race difficulty using a rubber-band system
11
Other Books You May Enjoy
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Generating enemies with the evolutionary algorithm

So far, we have created topological algorithms, so it's time to explore a different kind of content, such as enemies.

In this recipe, we will make use of the evolutionary algorithm to create waves of enemies based on the ones that are difficult to tackle for the player.

Getting ready

We need to define the template for the enemies to focus the next section on how to implement the main evolutionary algorithm. In this case, we will use a serializable class named EvolEnemy:

using UnityEngine;
using UnityEngine.UI;

[System.Serializable]
public class EvolEnemy
{
  public Sprite sprite;
  public int healthInit;
  public int healthMax;
  public int healthVariance;
}
...
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