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Unity 2017 Game AI Programming - Third Edition

Unity 2017 Game AI Programming - Third Edition - Third Edition

By : Raymundo Barrera, Aung Sithu Kyaw, Thet Naing Oo
2.8 (4)
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Unity 2017 Game AI Programming - Third Edition

Unity 2017 Game AI Programming - Third Edition

2.8 (4)
By: Raymundo Barrera, Aung Sithu Kyaw, Thet Naing Oo

Overview of this book

Unity 2017 provides game and app developers with a variety of tools to implement Artificial Intelligence. 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 third edition with Unity will help you break down Artificial Intelligence 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 5. Further on you will learn 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 will then learn how to implement simple flocks and crowd's dynamics, key AI concepts. Moving on, you will learn how to implement a behavior tree through a game-focused example. Lastly, you'll combine fuzzy logic concepts with state machines and apply all the concepts in the book to build a simple tank game.
Table of Contents (14 chapters)
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Title Page
Packt Upsell
Contributors
Preface
2
Index

Testing our framework


The framework that we just reviewed is nothing more than this. It provides us with all the functionality we need to make a tree, but we have to make the actual tree ourselves. For the purposes of this book, a somewhat manually constructed tree is provided.

Planning ahead

Before we set up our tree, let's look at what we're trying to accomplish. It is often helpful to visualize a tree before implementing it. Our tree will count up from zero to a specified value. Along the way, it will check whether certain conditions are met for that value and report its state accordingly. The following diagram illustrates the basic hierarchy for our tree:

For our tests, we will use a three-tier tree, including the root node:

  • Node 1: This is our root node. It has children, and we want to be able to return a success if any of the children are a success, so we'll implement it as a selector.
  • Node 2a: We'll implement this node using an ActionNode.
  • Node 2b: We'll use this node to demonstrate how...

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