Book Image

Unity 5.x Game AI Programming Cookbook

By : Jorge Palacios
5 (1)
Book Image

Unity 5.x Game AI Programming Cookbook

5 (1)
By: Jorge Palacios

Overview of this book

Unity 5 comes fully packaged with a toolbox of powerful features to help game and app developers create and implement powerful game AI. Leveraging these tools via Unity’s API or built-in features allows limitless possibilities when it comes to creating your game’s worlds and characters. This practical Cookbook covers both essential and niche techniques to help you be able to do that and more. This Cookbook is engineered as your one-stop reference to take your game AI programming to the next level. Get to grips with the essential building blocks of working with an agent, programming movement and navigation in a game environment, and improving your agent's decision making and coordination mechanisms - all through hands-on examples using easily customizable techniques. Discover how to emulate vision and hearing capabilities for your agent, for natural and humanlike AI behaviour, and improve them with the help of graphs. Empower your AI with decision-making functions through programming simple board games such as Tic-Tac-Toe and Checkers, and orchestrate agent coordination to get your AIs working together as one.
Table of Contents (15 chapters)
Unity 5.x Game AI Programming Cookbook
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Index

Introduction


In this chapter, we will learn different approaches on how to simulate sense stimuli on an agent. We will learn how to use tools that we are already familiar with to create these simulations: colliders, and graphs.

On the first approach, we will take advantage of ray casting, colliders, and the MonoBehaviour functions bound to this component, such as OnCollisionEnter, in order to leverage the need to acquire objects nearby in the three-dimensional world. Then, we will learn how to simulate the same stimuli using the graph theory and functions so that we can take advantage of this way of representing the world.

Finally, we'll learn how to implement agent awareness using a mixed approach that considers the previously learned sensory-level algorithms.