Artificial Intelligence

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Stuart Russell was born in 1962 in Portsmouth, England. He received his B.A. with first-class honours in physics from Oxford University in 1982, and his Ph.D. in computer science from Stanford in 1986. He then joined the faculty of the University of California at Berkeley, where he is a professor of computer science, director of the Center for Intelligent Systems, and holder of the Smith–Zadeh Chair in Engineering. In 1990, he received the Presidential Young Investigator Award of the National Science Foundation, and in 1995 he was cowinner of the Computers and Thought Award. He was a 1996 Miller Professor of the University of California and was appointed to a Chancellor’s Professorship in 2000. In 1998, he gave the Forsythe Memorial Lectures at Stanford University. He is a Fellow and former Executive Council member of the American Association for Artificial Intelligence. He has published over 100 papers on a wide range of topics in artificial intelligence. His other books include The Use of Knowledge in Analogy and Induction and (with Eric Wefald) Do the Right Thing: Studies in Limited Rationality.

Peter Norvig is currently Director of Research at Google, Inc., and was the director responsible for the core Web search algorithms from 2002 to 2005. He is a Fellow of the American Association for Artificial Intelligence and the Association for Computing Machinery. Previously, he was head of the Computational Sciences Division at NASA Ames Research Center, where he oversaw NASA’s research and development in artificial intelligence and robotics, and chief scientist at Junglee, where he helped develop one of the first Internet information extraction services. He received a B.S. in applied mathematics from Brown University and a Ph.D. in computer science from the University of California at Berkeley. He received the Distinguished Alumni and Engineering Innovation awards from Berkeley and the Exceptional Achievement Medal from NASA. He has been a professor at the University of Southern California and a research faculty member at Berkeley. His other books are Paradigms of AI Programming: Case Studies in Common Lisp and Verbmobil: A Translation System for Face-to-Face Dialog and Intelligent Help Systems for UNIX.

出版者:Pearson
作者:Stuart Russell
出品人:
页数:1152
译者:
出版时间:2009-12-11
价格:USD 195.00
装帧:Hardcover
isbn号码:9780136042594
丛书系列:
图书标签:
  • 人工智能 
  • AI 
  • Artificial.Intelligence 
  • 计算机 
  • 计算机科学 
  • CS 
  • 算法 
  • 机器学习 
  •  
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The long-anticipated revision of this #1 selling book offers the most comprehensive, state of the art introduction to the theory and practice of artificial intelligence for modern applications. Intelligent Agents. Solving Problems by Searching. Informed Search Methods. Game Playing. Agents that Reason Logically. First-order Logic. Building a Knowledge Base. Inference in First-Order Logic. Logical Reasoning Systems. Practical Planning. Planning and Acting. Uncertainty. Probabilistic Reasoning Systems. Making Simple Decisions. Making Complex Decisions. Learning from Observations. Learning with Neural Networks. Reinforcement Learning. Knowledge in Learning. Agents that Communicate. Practical Communication in English. Perception. Robotics. For computer professionals, linguists, and cognitive scientists interested in artificial intelligence.

具体描述

读后感

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疯狂的书~~~ 深度和广度俱佳, 理论讲解透彻,实践细节准确, 语言通俗,对人工智能的归类和划分有启发意义。  

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国内的人民邮电出过一本中译版,说老实话翻译的很差,非常影响阅读 如果真的有心读这本书的话,还是要看英文原版 这本书是一本指导性的AI书籍,哪个方向都涉及的不深,不过当需要查阅资料,尤其是概念性的资料的时候,这本书却是一个很不错的选择  

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这本书居然04年就出了,而且出了中文版。为什么我那时就没有找到这本书呢?不然现在的我可能就不是今天的我。 当然,一个很大的问题是:那时的我看了这本书以后能够看得懂吗?就算那时我可以解除到这本书,那时的我到底会怎样的对待呢?  

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这本书不是很好懂的,对于自学的初学者而言。我自学的,看这本书,半懂不懂的,最大的困难还是在逻辑那一块吧。这本书很全面,虽然不敢说把人工智能(包括机器学习)领域的一切都包括了吧,但是至少概况是都覆盖到了。或许正是这么全面的原因,也或许是译者翻译的原因,也有...  

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用户评价

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三主线,强化学习,图模型,神经网络

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粗略翻了一下,其实我不是很喜欢这种大而全的书,但不得不说作者太nb了,基本啥都涉及到,而且都点到了重点,这本书真适合买来收藏

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这学期的教材,读了大概50-60%的内容。大而全,优点是面面俱到,缺点是无法dive into details. 脉络还是比较清晰的,从Agent出发,探讨不同的问题和子领域。像Search, Adversarial Search和Planning这几章写的就很有味道。但是一些比较偏计算的内容如HMM,显然不够精细,有时候也有冗文之嫌,这些地方还是建议参考其他材料。总的来说是Best Introduction to AI as a whole.

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这学期的教材,读了大概50-60%的内容。大而全,优点是面面俱到,缺点是无法dive into details. 脉络还是比较清晰的,从Agent出发,探讨不同的问题和子领域。像Search, Adversarial Search和Planning这几章写的就很有味道。但是一些比较偏计算的内容如HMM,显然不够精细,有时候也有冗文之嫌,这些地方还是建议参考其他材料。总的来说是Best Introduction to AI as a whole.

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老美写书能不能不要这么啰嗦!!!

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