Procedural Content Generation Via Machine Learning: An Overview

Guzdial, Matthew, Snodgrass, Sam, Summerville, Adam J.

Procedural Content Generation Via Machine Learning: An Overview
  • 出版商: Springer
  • 出版日期: 2023-12-07
  • 售價: $2,750
  • 貴賓價: 9.5 折 $2,613
  • 語言: 英文
  • 頁數: 238
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 303116721X
  • ISBN-13: 9783031167218
  • 相關分類: Machine Learning

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商品描述

This book surveys current and future approaches to generating video game content with machine learning or Procedural Content Generation via Machine Learning (PCGML). Machine learning is having a major impact on many industries, including the video game industry. PCGML addresses the use of computers to generate new types of content for video games (game levels, quests, characters, etc.) by learning from existing content. The authors illustrate how PCGML is poised to transform the video games industry and provide the first ever beginner-focused guide to PCGML. This book features an accessible introduction to machine learning topics, and readers will gain a broad understanding of currently employed PCGML approaches in academia and industry. The authors provide guidance on how best to set up a PCGML project and identify open problems appropriate for a research project or thesis. This book is written with machine learning and games novices in mind and includes discussions of practical and ethical considerations along with resources and guidance for starting a new PCGML project.

商品描述(中文翻譯)

本書概述了利用機器學習或機器學習生成程序性內容生成（PCGML）的方法來生成視頻遊戲內容的現有和未來方法。機器學習對許多行業產生了重大影響，包括視頻遊戲行業。PCGML通過從現有內容中學習，解決了使用計算機生成視頻遊戲的新類型內容（遊戲關卡、任務、角色等）的問題。作者們說明了PCGML如何改變視頻遊戲行業，並提供了第一本針對初學者的PCGML指南。本書介紹了機器學習主題，讀者將對目前在學術界和工業界使用的PCGML方法有廣泛的了解。作者們提供了如何建立一個PCGML項目以及確定適合研究項目或論文的開放問題的指導。本書針對機器學習和遊戲初學者撰寫，包括實際和道德考慮的討論，以及開始一個新的PCGML項目的資源和指導。

作者簡介

Matthew Guzdial, Ph.D, is an Assistant Professor in the Computing Science Department at the University of Alberta and a Canada CIFAR AI Chair at the Alberta Machine Intelligence Institute (Amii). His research focuses on the intersection of machine learning, creativity, and human-centered computing. He is a recipient of an Early Career Researcher Award from NSERC, a Unity Graduate Fellowship, and two best conference paper awards from the International Conference on Computational Creativity. His work has been featured in the BBC, WIRED, Popular Science, and Time.
Sam Snodgrass is an AI researcher at modl.ai, a game AI company focused on bringing state of the art game AI research from academia to the games industry. His research focuses on making PCGML more accessible to non-ML experts. This work includes making PCGML systems more adaptable and self-reliant, reducing the authorial burden of creating training data through domain blending, and building tools that allow for easier interactions with the underlying PCGML systems and their outputs. Through his work at modl.ai he has deployed several mixed-initiative PCGML tools into game studios to assist with level design and creation.

Adam Summerville is the lead AI engineer for Procedural Content Generation at The Molasses Flood, a CD Projekt studio. Prior to this, he was an assistant professor at California State Polytechnic University, Pomona. His research focuses on the intersection of artificial intelligence in games with a high-level goal of enabling experiences that would not be possible without artificial intelligence. This research ranges from procedural generation of levels, social simulation for games, and the use of natural language processing for gameplay. His work has been shown at the SF MoMA and SlamDance and won the audience choice award at IndieCade.


作者簡介(中文翻譯)

Matthew Guzdial博士是阿爾伯塔大學計算科學系的助理教授，也是阿爾伯塔機器智能研究所（Amii）的加拿大CIFAR AI主席。他的研究專注於機器學習、創造力和以人為本的計算的交叉領域。他曾獲得NSERC的早期職業研究者獎、Unity研究生獎學金以及國際計算創造力研討會的兩項最佳論文獎。他的工作曾在BBC、WIRED、Popular Science和Time等媒體中亮相。

Sam Snodgrass是modl.ai的人工智能研究員，該公司專注於將學術界的最新遊戲人工智能研究應用於遊戲行業。他的研究專注於使非機器學習專家更容易使用PCGML（Procedural Content Generation with Machine Learning）。這項工作包括使PCGML系統更具適應性和自主性，通過領域融合減輕創建訓練數據的負擔，以及建立工具，使與底層PCGML系統及其輸出的交互更加容易。通過在modl.ai的工作，他已經將幾個混合倡議的PCGML工具部署到遊戲工作室，以協助關卡設計和創作。

Adam Summerville是The Molasses Flood的程序生成AI工程師，該公司是CD Projekt的一家工作室。在此之前，他曾是加州州立理工大學波莫納分校的助理教授。他的研究專注於遊戲中人工智能與實現無法在沒有人工智能的情況下實現的體驗之間的交叉領域。這項研究涵蓋了關卡的程序生成、遊戲的社交模擬以及自然語言處理在遊戲中的應用。他的作品曾在舊金山現代藝術博物館和SlamDance展出，並在IndieCade上獲得觀眾選擇獎。

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