An Introduction to Universal Artificial Intelligence (普遍人工智慧導論)
Hutter, Marcus, Quarel, David, Catt, Elliot
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)
《通用人工智慧簡介》提供了一個正式的基礎，解釋了代理人在未知環境中如何以智能方式行動。該概念最初在《通用演算法智能》（Hutter, 2000）中提出，通用人工智慧（UAI）提供了一個框架，幾乎可以將所有人工智慧問題都加以表述，並提供了解決這些問題的理論。UAI結合了連續決策理論、貝葉斯推論和演算資訊理論的思想，構建了AIXI，一個在未知環境中學習以最佳方式行動的最優強化學習代理人。AIXI是智能行為的理論黃金標準。
本書涵蓋了UAI的理論和實踐兩個方面。可以使用上下文樹加權進行高效的貝葉斯更新，並可以使用蒙特卡羅樹搜索進行規劃的近似。書中提供了讀者實現的演算法和相應的實驗結果進行比較。這些演算法用於近似AIXI。書末還就人工通用智能進行了哲學性的討論：超智能代理人是否能夠被建構？它們是否不可避免地會被建構，以及可能的後果是什麼？
本書適合高年級本科生閱讀。它提供了一個詳盡的章節，填補了所需的數學、概率、資訊和可計算性理論背景知識。
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DeepMind倫敦分部的高級研究員，也是澳大利亞國立大學（ANU）研究學院計算機科學系（RSCS）的教授（全職至2019年，此後榮譽教授）。他是正在進行的人類知識壓縮競賽的主席。他於1992年從慕尼黑工業大學獲得計算機科學碩士學位，於1996年獲得理論粒子物理學博士學位，並於2003年完成了他的博士後資格。在開始學術生涯之前，他在多家公司擔任活躍的軟件開發人員多年。他於2000年在瑞士盧加諾的人工智能（AI）研究所IDSIA開始了他的學術生涯，並在那裡待了六年。自2000年以來，他主要從事人工智能的基礎問題研究，發表了200多篇同行評審的研究論文，並出版了他的著作《通用人工智能》（Springer，EATCS，2005）。他曾擔任多個會議的PC成員、主席和組織者，並為主要會議和期刊審稿。他發表了多次邀請演講，並且他在人工智能和統計學方面的工作被提名並獲得了幾個獎項（UAI，IJCAI-JAIR，AGI Kurzweil，Lindley）。http://www.hutter1.net/
David Quarel正在澳大利亞國立大學攻讀博士學位。他擁有數學學士學位和計算機科學碩士學位，專攻人工智能和機器學習。David在開發課程內容和提煉適合廣泛學術觀眾的複雜主題方面有數年的經驗，並在澳大利亞國立大學發表過客座演講，在開始攻讀博士學位之前擔任全職導師兩年。
Elliot Catt是DeepMind倫敦分部的研究科學家，之前完成了通用人工智能的博士學位。他擁有數學學士學位和碩士學位，以及計算機科學博士學位。Elliot曾在澳大利亞國立大學講授過高級人工智能課程，並在通用人工智能主題上發表了多篇作品。https://catt.id/
