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)

Representing the world with grids

A grid is the most widely-used structure for representing worlds in games because it is easy to implement and visualize. However, we will lay the foundations for advanced graph representations while learning the basis of graph theory and its properties.

Getting ready

First, we need to create an abstract class, Graph, declaring the virtual methods that every graph representation implements. It is done this way because no matter how the vertices and edges are represented internally, the path-finding algorithms remain high level, thereby avoiding implementation of the algorithms for each type of graph representation.

This class works as a parent class for the different representations to be learned...