Book Image

Unity Artificial Intelligence Programming - Fifth Edition

By : Dr. Davide Aversa
Book Image

Unity Artificial Intelligence Programming - Fifth Edition

By: Dr. Davide Aversa

Overview of this book

Developing artificial intelligence (AI) for game characters in Unity has never been easier. Unity provides game and app developers with a variety of tools to implement AI, from 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 game worlds and characters. The updated fifth edition of Unity Artificial Intelligence Programming starts by breaking down AI into simple concepts. 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. As you progress, you’ll learn how to implement a finite state machine (FSM) to determine how your AI behaves, apply probability and randomness to make games less predictable, and implement a basic sensory system. Later, you’ll understand how to set up a game map with a navigation mesh, incorporate movement through techniques such as A* pathfinding, and provide characters with decision-making abilities using behavior trees. By the end of this Unity book, you’ll have the skills you need to bring together all the concepts and practical lessons you’ve learned to build an impressive vehicle battle game.
Table of Contents (17 chapters)
1
Part 1:Basic AI
6
Part 2:Movement and Navigation
11
Part 3:Advanced AI

Chapter 5: Flocking

During early summer evenings, you have probably seen flocks of birds flying in the sky. You have probably noted how they seem to move as a single living object: they all move in a particular direction, turn around, and grow and shrink. A flocking system aims to replicate this behavior in games: we want to implement an algorithm to move many objects as an organic group.

In games, we call each element of a flock a boid. To implement a flocking behavior, we do not need to equip each boid with a high-level complex decision-making system; instead, all we need to do is implement simple reactive rules for each boid that depend only on the state of the flock itself. Thus, flocking is an excellent example of emergent behavior: each boid reacts exclusively to its neighbor's behaviors; nevertheless, the flock seems to move as if someone were coordinating it.

In this chapter, we will learn what these rules are and how to implement them in Unity3D. We will implement...