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Artificial Intelligence in Unreal Engine 5

You're reading from   Artificial Intelligence in Unreal Engine 5 Unleash the power of AI for next-gen game development with UE5 by using Blueprints and C++

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Product type Paperback
Published in Oct 2024
Publisher Packt
ISBN-13 9781836205852
Length 358 pages
Edition 1st Edition
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Author (1):
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Marco Secchi Marco Secchi
Author Profile Icon Marco Secchi
Marco Secchi
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Table of Contents (22) Chapters Close

Preface 1. Part 1: Introducing Artificial Intelligence in Games
2. Chapter 1: Getting Started with AI Game Development FREE CHAPTER 3. Chapter 2: Introducing the Unreal Engine AI System 4. Part 2: Understanding the Navigation System
5. Chapter 3: Presenting the Unreal Engine Navigation System 6. Chapter 4: Setting Up a Navigation Mesh 7. Chapter 5: Improving Agent Navigation 8. Chapter 6: Optimizing the Navigation System 9. Part 3: Working with Decision Making
10. Chapter 7: Introducing Behavior Trees 11. Chapter 8: Setting Up a Behavior Tree 12. Chapter 9: Extending Behavior Trees 13. Chapter 10: Improving Agents with the Perception System 14. Chapter 11: Understanding the Environment Query System 15. Part 4: Exploring Advanced Topics
16. Chapter 12: Using Hierarchical State Machines with State Trees 17. Chapter 13: Implementing Data-Oriented Calculations with Mass 18. Chapter 14: Implementing Interactable Elements with Smart Objects 19. Index 20. Other Books You May Enjoy Appendix – Understanding C++ in Unreal Engine

Understanding pathfinding

As you are aware, Unreal Engine uses pathfinding for moving an agent around a level; in this section, I will go a bit deeper into detail on how things work under the hood. Unreal Engine takes advantage of a generalized version of the A* algorithm, a widely employed graph traversal and pathfinding algorithm in computer science. Known for its completeness, optimality, and efficiency, its main goal is to determine the shortest path between a designated source node and a specified goal node in a weighted graph.

This graph is a node-based representation of the level, where nodes represent walkable areas that are interconnected and have information on neighbor nodes and traversal costs to reach them.

A* uses a heuristic function to estimate the cost from each node to the target location; this trial-by-error system helps guide the search toward the most promising paths, improving efficiency.

During the pathfinding process, the algorithm maintains two lists...

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