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Unity Artificial Intelligence Programming

Unity Artificial Intelligence Programming

By : Dr. Davide Aversa , Aung Sithu Kyaw, Peters
4.3 (3)
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Unity Artificial Intelligence Programming

Unity Artificial Intelligence Programming

4.3 (3)
By: Dr. Davide Aversa , Aung Sithu Kyaw, Peters

Overview of this book

Developing Artificial Intelligence (AI) for game characters in Unity 2018 has never been easier. Unity provides game and app developers with a variety of tools to implement AI, from the 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 your game's worlds and characters. This fourth edition with Unity will help you break down AI into simple concepts to give you a fundamental understanding of the topic to build upon. 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. Further on, you'll learn how to distinguish the state machine pattern and implement one of your own. This is followed by learning how to implement a basic sensory system for your AI agent and coupling it with a Finite State Machine (FSM). Next, you'll learn how to use Unity's built-in NavMesh feature and implement your own A* pathfinding system. You'll then learn how to implement simple ?ocks and crowd dynamics, which are key AI concepts in Unity. Moving on, you'll learn how to implement a behavior tree through a game-focused example. Lastly, you'll apply all the concepts in the book to build a popular game.
Table of Contents (13 chapters)
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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 selectively raycast against a particular layer. 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, and by using some vehicle models combined with obstacle avoidance behavior, we can easily set up a decent traffic simulation. 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...

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