Book Image

Unity Artificial Intelligence Programming - Fourth Edition

By : Dr. Davide Aversa, Aung Sithu Kyaw, Clifford Peters
Book Image

Unity Artificial Intelligence Programming - Fourth Edition

By: Dr. Davide Aversa, Aung Sithu Kyaw, Clifford Peters

Overview of this book

Developing Artificial Intelligence (AI) for game characters in Unity 2018 has never been easier. Unity provides game and app developers with a variety of tools to implement AI, from the 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 your game's worlds and characters. This fourth edition with Unity will help you break down AI into simple concepts to give you a fundamental understanding of the topic to build upon. 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. Further on, you'll learn how to distinguish the state machine pattern and implement one of your own. This is followed by learning how to implement a basic sensory system for your AI agent and coupling it with a Finite State Machine (FSM). Next, you'll learn how to use Unity's built-in NavMesh feature and implement your own A* pathfinding system. You'll then learn how to implement simple ?ocks and crowd dynamics, which are key AI concepts in Unity. Moving on, you'll learn how to implement a behavior tree through a game-focused example. Lastly, you'll apply all the concepts in the book to build a popular game.
Table of Contents (13 chapters)

Navigation areas

In games with complex environments, we usually have some areas that are harder to traverse than others. For example, crossing a lake over the water is definitely harder than crossing it with a bridge. To simulate this, we want to make crossing the lake more costly than using a bridge. In this section, we will look at Navigation areas which are used as , a way to define different layers with different navigation cost values.

For this, we, build a scene, as shown in the following screenshot. There are three planes to represent two ground planes connected by a bridge-like structure, and a water plane between them. As you can see, crossing over the water plane is the most direct way to traverse the lake; however, passing through the water costs more than using the bridge and, therefore, the pathfinding algorithm will prefer the bridge over the water:

Scene with layers...