Procedural Content Generation Via Machine Learning: An Overview
暫譯: 透過機器學習的程序性內容生成：概述
Guzdial, Matthew, Snodgrass, Sam, Summerville, Adam
- 出版商: Springer
- 出版日期: 2025-05-31
- 售價: $1,840
- 貴賓價: 9.5 折 $1,748
- 語言: 英文
- 頁數: 295
- 裝訂: Hardcover - also called cloth, retail trade, or trade
- ISBN: 3031847555
- ISBN-13: 9783031847554
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相關分類:
Machine Learning
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相關主題
商品描述
This second edition updates and expands upon the first beginner-focused guide to Procedural Content Generation via Machine Learning (PCGML), which is 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 survey current and future approaches to generating video game content and illustrate the major impact that PCGML has had on video games industry. In order to provide the most up-to-date information, this new edition incorporates the last two years of research and advancements in this rapidly developing area. The book guides readers on how best to set up a PCGML project and identify open problems appropriate for a research project or thesis. The authors discuss the practical and ethical considerations for PCGML projects and demonstrate how to avoid the common pitfalls. This second edition also introduces a new chapter on Generative AI, which covers the benefits, risks, and methods for applying pre-trained transformers to PCG problems.
商品描述(中文翻譯)
本書第二版更新並擴展了第一版針對初學者的《透過機器學習進行程序內容生成（PCGML）》指南，該指南探討了如何利用計算機從現有內容中學習，生成新類型的視頻遊戲內容（如遊戲關卡、任務、角色等）。作者調查了當前及未來生成視頻遊戲內容的方法，並說明了PCGML對遊戲產業的重大影響。為了提供最新的信息，本新版本納入了過去兩年在這一快速發展領域的研究和進展。本書指導讀者如何最佳地設置PCGML項目，並識別適合研究項目或論文的開放問題。作者討論了PCGML項目的實際和倫理考量，並展示了如何避免常見的陷阱。本第二版還新增了一章關於生成式人工智慧，涵蓋了應用預訓練變壓器於PCG問題的好處、風險和方法。
作者簡介
Matthew Guzdial, Ph.D., is an Assistant Professor in the Computing Science department of 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, Ph.D., is the Manager of the Applied AI team 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, Ph.D., 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, SlamDance, and won the audience choice award at IndieCade.
作者簡介(中文翻譯)
馬修·古茲迪爾（Matthew Guzdial），博士，是阿爾伯塔大學計算科學系的助理教授，並擔任阿爾伯塔機器智能研究所（Alberta Machine Intelligence Institute, Amii）的加拿大CIFAR AI主席。他的研究專注於機器學習、創造力與以人為中心的計算之間的交集。他曾獲得NSERC的早期職業研究者獎、Unity研究生獎學金，以及國際計算創意會議的兩項最佳會議論文獎。他的研究成果曾被BBC、WIRED、Popular Science和Time等媒體報導。
山姆·斯諾德格拉斯（Sam Snodgrass），博士，是modl.ai的應用AI團隊經理，該公司專注於將最先進的遊戲AI研究從學術界帶入遊戲產業。他的研究重點在於使程序生成機器學習（PCGML）對非機器學習專家更具可及性。這項工作包括使PCGML系統更具適應性和自給自足，通過領域融合減少創建訓練數據的作者負擔，以及構建工具以便於與基礎PCGML系統及其輸出進行更輕鬆的互動。通過在modl.ai的工作，他已將幾個混合主動的PCGML工具部署到遊戲工作室，以協助關卡設計和創建。
亞當·薩默維爾（Adam Summerville），博士，是The Molasses Flood（CD Projekt工作室）程序內容生成的首席AI工程師。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的人工智能交集，目標是實現沒有人工智能無法實現的體驗。這項研究涵蓋了關卡的程序生成、遊戲的社會模擬，以及自然語言處理在遊戲玩法中的應用。他的作品曾在舊金山現代藝術博物館（SF MoMA）、SlamDance展出，並在IndieCade獲得觀眾選擇獎。
