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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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Revisiting the A* algorithm

Let's review the A* algorithm again, before we proceed to implement it in the next section. The foundation of any pathfinding algorithm is a representation of the world. Pathfinding algorithms cannot search over the noisy structure of polygons in the game map; instead, we need to provide them with a simplified version of the world in which we identify the locations that can be traversed by the agent, and the ones that are inaccessible.

There are many ways of doing this; however, for this example, we will use one of the most straightforward solutions: a 2D grid. Therefore, we will implement the GridManager class in order to convert the real map into a 2D tile representation. The GridManager class keeps a list of Node objects, representing a single tile in the 2D grid. Of course, we need to implement the Node class too: this class stores node information...

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