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

Summary

In this chapter, we learned how to implement flocking behaviors in two ways. First, we examined and learned how to implement a basic flocking algorithm using nothing other than our scripts. Next, we implemented the same algorithm using Unity's Rigidbody component to control the boid's movement and Sphere Collider to avoid collision with other boids.

In our example, we always referred to boids as bird-like entities. However, we can use flocking for many other applications: fishes swimming in the sea, sheep grazing on a plane, a swarm of insects, and even groups of people walking on the street can show flocking behavior. To adapt the algorithm to different scenarios, we just need to change the flocking rules' values and eventually lock the movement to a plane.

In the next chapter, we will go beyond random movement and look at how to follow a specific path. This is the first step toward learning how to avoid obstacles that are in your way.