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相關主題
商品描述
An Introduction to Universal Artificial Intelligence provides the formal underpinning of what it means for an agent to act intelligently in an unknown environment. First presented in Universal Algorithmic Intelligence (Hutter, 2000), UAI offers a framework in which virtually all AI problems can be formulated, and a theory of how to solve them. UAI unifies ideas from sequential decision theory, Bayesian inference, and algorithmic information theory to construct AIXI, an optimal reinforcement learning agent that learns to act optimally in unknown environments. AIXI is the theoretical gold standard for intelligent behavior.
The book covers both the theoretical and practical aspects of UAI. Bayesian updating can be done efficiently with context tree weighting, and planning can be approximated by sampling with Monte Carlo tree search. It provides algorithms for the reader to implement, and experimental results to compare against. These algorithms are used to approximate AIXI. The book ends with a philosophical discussion of Artificial General Intelligence: Can super-intelligent agents even be constructed? Is it inevitable that they will be constructed, and what are the potential consequences?
This text is suitable for late undergraduate students. It provides an extensive chapter to fill in the required mathematics, probability, information, and computability theory background.
商品描述(中文翻譯)
《通用人工智慧導論》提供了代理在未知環境中智能行動的正式基礎。這一概念最早在《通用演算法智慧》（Hutter, 2000）中提出，UAI 提供了一個框架，幾乎所有的人工智慧問題都可以在此框架中進行表述，並且有理論來解決這些問題。UAI 統合了序列決策理論、貝葉斯推斷和演算法信息理論的思想，構建了 AIXI，一個最佳強化學習代理，能夠在未知環境中學習如何最佳行動。AIXI 是智能行為的理論金標準。
本書涵蓋了 UAI 的理論和實踐方面。貝葉斯更新可以通過上下文樹加權有效地進行，而規劃可以通過蒙地卡羅樹搜索進行抽樣來近似。書中提供了讀者可以實現的演算法，以及可供比較的實驗結果。這些演算法用於近似 AIXI。本書最後以哲學討論結束，探討人工通用智慧：超智能代理是否真的能夠被構建？它們的構建是否不可避免，並且可能帶來什麼後果？
本書適合高年級本科生。它提供了一個廣泛的章節，以填補所需的數學、概率、信息和可計算性理論背景。
作者簡介
Marcus Hutter is Senior Researcher at DeepMind in London and Professor in the Research School of Computer Science (RSCS) at the Australian National University (ANU) in Canberra, Australia (fulltime till 2019 and honorary since then). He is Chair of the ongoing Human Knowledge Compression Contest. He received a master's degree in computer science in 1992 from the University of Technology in Munich, Germany, a PhD in theoretical particle physics in 1996, and completed his Habilitation in 2003. He worked as an active software developer for various companies in several areas for many years, before he commenced his academic career in 2000 at the Artificial Intelligence (AI) institute IDSIA in Lugano, Switzerland, where he stayed for six years. Since 2000, he has mainly worked on fundamental questions in AI resulting in over 200 peer-reviewed research publications and his book "Universal Artificial Intelligence" (Springer, EATCS, 2005). He has served (as PC member, chair, organizer) for numerous conferences, and reviews for major conferences and journals. He has given numerous invited lectures and his work in AI and statistics was nominated for and received several awards (UAI, IJCAI-JAIR, AGI Kurzweil, Lindley). http: //www.hutter1.net/
David Quarel is completing a PhD at the ANU. He holds a BSc in mathematics and MSc in computer science, specialising in artificial intelligence and machine learning. David has several years' experience in developing course content and distilling complex topics suitable for a wide range of academic audiences, as well as having delivered guest lectures at the ANU, and spent two years as a full-time tutor before starting his PhD.
Elliot Catt is a Research Scientist at DeepMind London and has previously completed a PhD in Universal Artificial Intelligence. He holds a BSc and MSc in mathematics and a PhD in computer science. Elliot has lectured on the topic of Advanced Artificial Intelligence at the ANU and published several pieces of work on the topic of Universal Artificial Intelligence. https: //catt.id/
作者簡介(中文翻譯)
**Marcus Hutter** 是位於倫敦的 DeepMind 的高級研究員，也是澳洲國立大學 (ANU) 研究計算機科學學院 (RSCS) 的教授（2019 年前全職，之後為名譽教授）。他是持續進行的人類知識壓縮競賽的主席。他於 1992 年在德國慕尼黑的科技大學獲得計算機科學碩士學位，1996 年獲得理論粒子物理學博士學位，並於 2003 年完成 Habilitation。他在多個領域的多家公司擔任活躍的軟體開發人員多年，然後於 2000 年在瑞士盧加諾的人工智慧 (AI) 研究所 IDSIA 開始他的學術生涯，並在那裡待了六年。自 2000 年以來，他主要研究 AI 的基本問題，發表了超過 200 篇經過同行評審的研究論文，以及他的著作《Universal Artificial Intelligence》（Springer, EATCS, 2005）。他曾擔任多個會議的程序委員會成員、主席和組織者，並為主要會議和期刊進行評審。他曾多次受邀演講，並因其在 AI 和統計學方面的工作獲得多個獎項提名及獲獎（UAI、IJCAI-JAIR、AGI Kurzweil、Lindley）。http://www.hutter1.net/
**David Quarel** 正在澳洲國立大學完成博士學位。他擁有數學學士學位和計算機科學碩士學位，專攻人工智慧和機器學習。David 擁有數年的課程內容開發經驗，能夠將複雜主題提煉成適合各種學術觀眾的內容，並曾在 ANU 進行客座講座，還在開始博士學位之前擔任了兩年的全職輔導員。
**Elliot Catt** 是 DeepMind 倫敦的研究科學家，並曾完成有關通用人工智慧的博士學位。他擁有數學學士和碩士學位，以及計算機科學博士學位。Elliot 曾在 ANU 講授高級人工智慧的課程，並在通用人工智慧主題上發表了多篇作品。https://catt.id/
