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

Basic flocking behavior

As we said in the introduction to this chapter, we can describe a flocking behavior by using just three intuitive properties:

  • Separation: This property, also called short-range repulsion, represents the minimum distance between neighboring boids to avoid collisions. You can imagine this rule as a force that pushes a boid away from the others.
  • Alignment: This property represents the likelihood for each boid to move in the same direction as the flock (we measure this as the average direction of all the individual boids).
  • Cohesion: This property, also called long-range attraction, represents the likelihood for each boid to move toward the center of mass of the flock (we measure this by averaging the position of each boid in the flock). Thus, you can imagine this rule as a force that pushes a boid toward the center of the flock.

In this demo, we will create a scene with flocks of objects and implement the flocking behavior in C#. For this...