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Unity Artificial Intelligence Programming

Unity Artificial Intelligence Programming

By : Dr. Davide Aversa
4.8 (4)
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Unity Artificial Intelligence Programming

Unity Artificial Intelligence Programming

4.8 (4)
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)
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1
Part 1:Basic AI
6
Part 2:Movement and Navigation
11
Part 3:Advanced AI

Baking navigation areas with different costs

In games with complex environments, we usually have areas that are harder to traverse than others. For example, crossing a lake with a bridge is less challenging than crossing it without a bridge. To simulate this, we want to make crossing the lake more costly than using a bridge. This section will look at navigation areas that define different layers with different navigation cost values.

For this, we build a scene, as shown in Figure 8.11. Three planes represent two ground planes separated by a water plane and connected by a bridge-like structure. 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 to the water:

Figure 8.11 – Scene with layers – NavMesh03-Layers.scene

Let's follow a step-by-step procedure so that we can...

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