人工智能 复杂问题求解的结构和策略 英文版 第6版

出版时间：2009-3  出版社：机械工业出版社  作者：卢格尔  页数：753  
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前言

I was very pleased to be asked to produce the sixth edition of my artificial intelligencebook. It is a compliment to the earlier editions, started over twenty years ago, that ourapproach to AI has been so highly valued. It is also exciting that, as new development inthe field emerges, we are able to present much of it in each new edition. We thank ourmany readers, colleagues, and students for keeping our topics relevant and our presenta-tion up to date.Many sections of the earlier editions have endured remarkably well, including thepresentation of logic, search algorithms, knowledge representation, production systems,machine learning, and, in the supplementary materials, the programming techniquesdeveloped in Lisp, Prolog, and with this edition, Java. These remain central to the practiceof artificial intelligence, and a constant in this new edition.This book remains accessible. We introduce key representation techniques includinglogic, semantic and connectionist networks, graphical models, and many more. Our searchalgorithms are presented clearly, first in pseudocode, and then in the supplementary mate-rials, many of them are implemented in Prolog, Lisp, and/or Java. It is expected that themotivated students can take our core implementations and extend them to new excitingapplications.We created, for the sixth edition, a new machine learning chapter based on stochasticmethods （Chapter 13）. We feel that the stochastic technology is having an increasinglylarger impact on AI, especially in areas such as diagnostic and prognostic reasoning, natu-ral language analysis, robotics, and machine learning.

内容概要

本书是一本经典的人工智能教材，全面阐述了人工智能的基础理论，有效结合了求解智能问题的数据结构以及实现的算法，把人工智能的应用程序应用于实际环境中，并从社会和哲学、心理学以及神经生理学角度对人工智能进行了独特的讨论。

作者简介

George F.Luger 1973年在宾夕法尼亚大学获得博士学位，并在之后的5年间在爱丁堡大学人工智能系进行博士后研究，现在是新墨西哥大学计算机科学研究、语言学及心理学教授。

书籍目录

Preface Publisher's Acknowledgements PART I ARTIFIClAL INTELLIGENCE：ITS ROOTS AND SCOPE  1  A1：HISTORY AND APPLICATIONS     1.1  From Eden to ENIAC：Attitudes toward Intelligence，Knowledge，andHuman Artifice      1.2  0verview ofAl Application Areas     1.3  Artificial Intelligence A Summary     1.4  Epilogue and References    1.5  Exercises  PART II ARTIFlClAL INTELLIGENCE AS REPRESENTATION AN D SEARCH   2 THE PREDICATE CALCULUS     2.0  Intr0血ction     2.1 The Propositional Calculus     2.2  The Predicate Calculus     2.3  Using Inference Rules to Produce Predicate Calculus Expressions     2.4  Application：A Logic—Based Financial Advisor     2.5  Epilogue and References     2.6  Exercises   3 STRUCTURES AND STRATEGIES FOR STATE SPACE SEARCH     3.0  Introducfion     3.1  GraphTheory     3.2  Strategies for State Space Search     3.3  using the state Space to Represent Reasoning with the Predicate Calculus      3.4  Epilogue and References      3.5  Exercises   4 HEURISTIC SEARCH     4.0  Introduction     4.l  Hill Climbing and Dynamic Programmin9     4.2 The Best-First Search Algorithm     4.3  Admissibility,Monotonicity,and Informedness     4.4  Using Heuristics in Games     4.5  Complexity Issues      4.6  Epilogue and References     4.7  Exercises   5  STOCHASTIC METHODS     5.0  Introduction     5.1 The Elements ofCountin9     5.2  Elements ofProbabilityTheory       5.3  Applications ofthe Stochastic Methodology     5.4  Bayes’Theorem      5.5  Epilogue and References     5.6  Exercises   6  coNTROL AND IMPLEMENTATION OF STATE SPACE SEARCH     6.0  Introduction  l93    6.1  Recursion.Based Search     6.2 Production Systems     6.3 The Blackboard Architecture for Problem Solvin9     6.4  Epilogue and References     6.5  Exercises PARTIII CAPTURING INTELLIGENCE：THE AI CHALLENGE   7  KNOWLEDGE REPRESENTATION     7.0  Issues in Knowledge Representation     7.1  A BriefHistory ofAI Representational Systems     ……  8 STRONG METHOD PROBLEM SOLVING  9 REASONING IN UNCERTAIN SITUATIONSPART Ⅳ MACHINE LEARNING  10 MACHINE LEARNING:SYMBOL-BASED  11 MACHINE LEARNING:CONNECTIONIST  12 MACHINE LEARNING:GENETIC AND EMERGENT  13 MACHINE LEARNING:PROBABILISTICPART Ⅴ ADVANCED TOPICS FOR AI PROBLEM SOLVING  14 AUTOMATED REASONING   15 UNDERSTANDING NATURAL LANGUAGEPART Ⅵ EPILOGUE  16 ARTIFICIAL INTELLIGENCE AS EMPIRICAL ENQUIRY

章节摘录

插图：postconditions of each action are in.the column below it. For example, row 5 lists the pre-conditions for pickup（X） and Column 6 lists the postconditions （the add and delete lists） ofpickup（X）. These postconditions are placed in the row of the action that uses them as pre-conditions, organizing them in a manner relevant to further actions. The triangle table'spurpose is to properly interleave the preconditions and postconditions of each of thesmaller actions that make up the larger goal. Thus, triangle tables address non-linearityissues in planning on the macro operator level; Partial-Order Planners （Russell and Norvig1995） and other approaches have further addressed these issues.One advantage of triangle tables is the assistance they can offer in attempting torecover from unexpected happenings, such as a block being slightly out of place, or acci-dents, such as dropping a block. Often an accident can require backing up several stepsbefore the plan can be resumed. When something goes wrong with a solution the plannercan go back into the rows and columns of the triangle table to check what is true. Once theplanner has figured out what is still true within the rows and columns, it then knows whatthe next step must be if the larger solution is to be restarted. This is formalized with thenotion of a kernel.The nth kernel is the intersection of all rows below and including the nth row and allcolumns to the left of and including the rtth column. In Figure 8.21 we have outlined thethird kernel in bold. In carrying out a plan represented in a triangle table, the ith operation（that is, the operation in row i） may be performed only if all predicates contained in the ithkernel aretrue. This offers a straightforward way of verifying that a step can be taken andalso supports systematic recovery from any disruption of the plan. Given a triangle table,we find and execute the highest-numbered action whose kernel is enabled.

媒体关注与评论

“在该领域里学生经常遇到许罗很难的概念，通过深刻的实例与简单明了的祝圈，该书清晰而准确垲阚述了这些概念。”　　——Toseph Lewis，圣迭戈州立大学“本书是人工智能课程的完美补充。它既给读者以历史的现点，又给幽所有莰术的宾用指南。这是一本必须要推荐的人工智能的田书。”　　——-Pascal Rebreyend，瑞典达拉那大学“该书的写作风格和全面的论述使它成为人工智能领域很有价值的文献。”　　——Malachy Eaton，利默里克大学

编辑推荐

《人工智能:复杂问题求解的结构和策略(英文版)(第6版)》是一本经典的人工智能教材，全面阐述了人工智能的基础理论，有效结合了求解智能问题的数据结构以及实现的算法，把人工智能的应用程序应用于实际环境中，并从社会和哲学、心理学以及神经生理学角度对人工智能进行了独特的讨论。《人工智能:复杂问题求解的结构和策略(英文版)(第6版)》新增内容新增一章，介绍用于机器学习的随机方法，包括一阶贝叶斯网络、各种隐马尔可夫模型，马尔可夫随机域推理和循环信念传播。介绍针对期望最大化学习以及利用马尔可夫链蒙特卡罗采样的结构化学习的参数选择，加强学习中马尔可夫决策过程的利用。介绍智能体技术和本体的使用。介绍自然语言处理的动态规划（Earley语法析器），以及Viterbi等其他概率语法分析技术。书中的许多算法采用Prolog．Lisp和Java语言来构建。

用户评论 (总计11条)

•   经典原版书。32开，但是印刷清晰。尤其是价格太划算了。卖家很好，发货也非常快。
•   书不错，不过是32开的~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
•   虽然没有《一种现代方法》那么经典，但还是好书！
•   　　导师推荐的人工智能入门书籍。首先，该书言简意赅比较容易读懂。第二，有很多例子穿插在在课文中，帮助读者能将每种人工智能的方法应用于只见众。第三，算法或者数据结构的解释被巧妙地阐释出来，而不是对一大堆资料的冗长的总结。最后，编程章节让学生能更深刻地理解资料，同时也穿插着许多对实现细节的参考。本书比较适合作为大学教材，在涉及的人工智能方面的内容非常广，唯一不足的可能是深度不足，读者通过该书入门之后可以阅读进阶书籍。
  　　PS:本书中的代码使用PROLOG和LISP，但是如果你不会他们也不需要买其他书了，因为书中有章节专门讲这两门语言的基础，所以在学习AI同时你也能学到对这两门语言的运用。
•   　　这么有趣的东西，当年的老师是如何讲到我睡着的呢，我很好奇。
  　　什么时候AI才会开始普遍渗透日常软件呢？
  　　自然语言处理、机器学习的技术应用到客户端的话，可以大大提高可用性，个人电脑完全可以成为我们的小秘嘛。
•   　　这本书有700多页,而且纸张不错,所以显得特别厚.
  　　我手头有Nilsson的那本薄很多的书,但是Luger这本充满了
  　　例子的更符合我的口味.去公司的公交车上的时间很漫长,
  　　这本书陪了我有一段时间,现在回想起来,还是很感谢Luger
  　　辛苦写了这么一本涵盖了人工智能很多方面的入门书.
  　　其中还有两章对人工智能常用语言prolog和lisp的介绍,
  　　尽管不长,但是放在这本书的背景下,能体现它们的一些特点.
  　　
•   图像识别也是AI嘛，Adobe中就有，蛮普及了
•   只有helloworld基础的适合么？
•   和编程基础似乎关系不大。其实除了我前面说到的两章，其他章节全是一些人工智能基本问题和技术的展示，只要对人工智能有兴趣，即可。当然，慢慢的用自己的helloworld版本来实现里面的技术，会更有好处--实际上，这是最后真正理解的必由之路。
•   大哥，你看的是英文还是中文？还有你说的那本薄的书是？？多谢。
•   机工的中文书啊。薄的也是。尼尔森吧，你搜索一下。