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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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Behavior Trees

In a previous chapter, we saw a basic but effective way to implement and manage character states and behaviors: Finite State Machines (FSMs). FSMs are simple to implement and very intuitive, but they have a fatal flaw: it is very hard to make them scale once states and transitions start getting numerous. For example, imagine a character that behaves differently depending on the amount of health and mana it has (high, medium, and low); we have a state when both health and mana are high, one in which, health is medium and, mana high, one in which they are both medium, and so on. In total, we have nine states just for that. If we add other conditions (such as player proximity, time of day, equipment, player’s score, or whatever you may imagine), the number of states grows exponentially.

Luckily, we have a solution: Behavior Trees (BTs). In essence, Behavior...

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