Jie Feng – författare

Create responsive and intelligent game AI using Blueprints in Unreal Engine 4

About This Book

Understand and apply your Game AI better through various projects such as adding randomness and probability, and introducing movementConfigure and debug Game AI logic using multiple methodologiesBridge the gap between your knowledge and Game AI in Unreal Engine 4

Who This Book Is For

This book is for programmers and artists who want to expand their knowledge of Game AI in relation to Unreal Engine 4. You are recommended to have some experience of exploring Unreal Engine 4 prior to this book because we jump straight into Game AI.

What You Will Learn

Understand the fundamental components of Game AI within Unreal Engine 4Skillfully introduce Game AI within Unreal Engine 4Configure, customize, and assign Navigation and AI components to your pawnCreate, debug, and analyze Game AI behaviorDesign responsive Game AI using the Behavior Tree methodologyCreate smart objects designed to interact with AIUtilize advanced AI features within your project to maximize the user experience

In Detail

Unreal Engine is a powerful game development engine that provides rich functionalities to create 2D and 3D games. Developers have the opportunity to build cross-platform mobile and desktop games from scratch. This book will show you how to apply artificial intelligence (AI) techniques to your Unreal project using blueprints as your scripting language. You will start with an introduction to AI, and learn how it is applied to gaming. Then you''ll jump right in and create a simple AI bot and apply basic behaviors to allow it to move randomly. As you progress, you''ll find out how to implement randomness and probability traits. Using NavMesh, you will impart navigation components such as character movement, MoveTo nodes, settings, and world objects, and implement Behavior Trees. At the end of the book, you will troubleshoot any issues that might crop up while building the game.

Style and approach

This easy-to-follow project-based guide throws you directly into the excitement of Game AI in an approachable and comprehensive manner.

This book constitutes the refereed post-conference proceedings of the 13th International Conference on Mobile Networks and Management, MONAMI 2023, held in Yingtan, China, in October 2023.The 21 full papers were carefully reviewed and selected from 41 submissions. The papers are divided into groups of content as follows: Wireless Communication and Networks; Network Security and Blockchain; Image Processing and Computer Vision; Distributed Computing; Emerging Applications.

This book constitutes the refereed post-conference proceedings of the 13th International Conference on Mobile Networks and Management, MONAMI 2023, held in Yingtan, China, in October 2023. The 21 full papers were carefully reviewed and selected from 41 submissions. The papers are divided into groups of content as follows: Wireless Communication and Networks; Network Security and Blockchain; Image Processing and Computer Vision; Distributed Computing; Emerging Applications.

This book investigates detailed hyperspectral image clustering using graph neural network (graph learning) methods, focusing on the overall construction of the model, design of self-supervised methods, image pre-processing, and feature extraction of graph information. Multiple graph neural network-based clustering methods for hyperspectral images are proposed, effectively improving the clustering accuracy of hyperspectral images and taking an important step towards the practical application of hyperspectral images. This book is innovative in content and emphasizes the integration of theory with practice, which can be used as a reference book for graduate students, senior undergraduate students, researchers, and engineering technicians in related majors such as electronic information engineering, computer application technology, automation, instrument science and technology, remote sensing.

This book investigates detailed hyperspectral image clustering using graph neural network (graph learning) methods, focusing on the overall construction of the model, design of self-supervised methods, image pre-processing, and feature extraction of graph information. Multiple graph neural network-based clustering methods for hyperspectral images are proposed, effectively improving the clustering accuracy of hyperspectral images and taking an important step towards the practical application of hyperspectral images. This book is innovative in content and emphasizes the integration of theory with practice, which can be used as a reference book for graduate students, senior undergraduate students, researchers, and engineering technicians in related majors such as electronic information engineering, computer application technology, automation, instrument science and technology, remote sensing.

This open access book addresses the current technical problems of low efficiency of emergency site information collection, lack of flexibility of emergency information interaction, lack of fusion analysis technology and disaster loss prediction model, and low intelligence of emergency auxiliary decision making. The content contains research on multiple information collection technology of power grid disaster loss, fusion analysis and prediction technology of power grid disaster loss information, and real-time information interaction technology between emergency site and command center in this work. This book illustrates the process of developing a prototype system for grid disaster perception and emergency command, which realizes the functions of grid disaster perception and emergency auxiliary decision-making and visualization command. The prototype intelligent perception and emergency command system for power grid disasters has been piloted in several units. It provided support for disaster loss prediction, disaster damage perception, and emergency command auxiliary decision-making in the earthquake in Sichuan, China, as well as the heavy rainfall in Zhejiang, China and Typhoon No. 9 "Lupi" that registered in Fujian, China, which significantly improved the emergency disposal Work efficiency.