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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

Summary

In this chapter, we set up two scenes and studied how to build path-following agents with obstacle avoidance behavior. We learned about the Unity3D layer feature and how to use Raycasts and SphereCasts against a particular layer selectively. Although these examples were simple, we can apply these simple techniques to various scenarios. For instance, we can set up a path along a road. We can easily set up a decent traffic simulation using some vehicle models combined with obstacle avoidance behavior. Alternatively, you could just replace them with biped characters and build a crowd simulation. You can also combine them with some finite state machines to add more behaviors and make them more intelligent.

The simple obstacle avoidance behavior that we implemented in this chapter doesn't consider the optimal path to reach the target position. Instead, it just goes straight to that target, and only if an obstacle is seen within a certain distance does it try to avoid it...

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