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.
Table of Contents (12 chapters)

Implementing artificial neural networks

Imagine that we made an enemy or game system that emulates the way the brain works. That's how neural networks operate. They are based on the neuron—we call it the Perceptron—and are formed of the sum of several neurons; its inputs and outputs are what makes a neural network.

In this recipe, we will learn how to build a neural system, starting from the Perceptron through to the way that they can be joined to create a network.

Getting ready...

We will need a data type for handling raw input; this is called InputPerceptron:

public class InputPerceptron 
{ 
    public float input; 
    public float weight; 
} 
...