人工智能 復(fù)雜問(wèn)題求解的結(jié)構(gòu)和策略 英文版 第6版

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.

內(nèi)容概要

本書(shū)是一本經(jīng)典的人工智能教材，全面闡述了人工智能的基礎(chǔ)理論，有效結(jié)合了求解智能問(wèn)題的數(shù)據(jù)結(jié)構(gòu)以及實(shí)現(xiàn)的算法，把人工智能的應(yīng)用程序應(yīng)用于實(shí)際環(huán)境中，并從社會(huì)和哲學(xué)、心理學(xué)以及神經(jīng)生理學(xué)角度對(duì)人工智能進(jìn)行了獨(dú)特的討論。

作者簡(jiǎn)介

George F.Luger 1973年在賓夕法尼亞大學(xué)獲得博士學(xué)位，并在之后的5年間在愛(ài)丁堡大學(xué)人工智能系進(jìn)行博士后研究，現(xiàn)在是新墨西哥大學(xué)計(jì)算機(jī)科學(xué)研究、語(yǔ)言學(xué)及心理學(xué)教授。

書(shū)籍目錄

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

章節(jié)摘錄

插圖：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.

媒體關(guān)注與評(píng)論

“在該領(lǐng)域里學(xué)生經(jīng)常遇到許羅很難的概念，通過(guò)深刻的實(shí)例與簡(jiǎn)單明了的祝圈，該書(shū)清晰而準(zhǔn)確塏闞述了這些概念?！薄　　猅oseph Lewis，圣迭戈州立大學(xué)“本書(shū)是人工智能課程的完美補(bǔ)充。它既給讀者以歷史的現(xiàn)點(diǎn)，又給幽所有莰術(shù)的賓用指南。這是一本必須要推薦的人工智能的田書(shū)。”　　——-Pascal Rebreyend，瑞典達(dá)拉那大學(xué)“該書(shū)的寫作風(fēng)格和全面的論述使它成為人工智能領(lǐng)域很有價(jià)值的文獻(xiàn)?！薄　　狹alachy Eaton，利默里克大學(xué)

編輯推薦

《人工智能:復(fù)雜問(wèn)題求解的結(jié)構(gòu)和策略(英文版)(第6版)》是一本經(jīng)典的人工智能教材，全面闡述了人工智能的基礎(chǔ)理論，有效結(jié)合了求解智能問(wèn)題的數(shù)據(jù)結(jié)構(gòu)以及實(shí)現(xiàn)的算法，把人工智能的應(yīng)用程序應(yīng)用于實(shí)際環(huán)境中，并從社會(huì)和哲學(xué)、心理學(xué)以及神經(jīng)生理學(xué)角度對(duì)人工智能進(jìn)行了獨(dú)特的討論?！度斯ぶ悄?復(fù)雜問(wèn)題求解的結(jié)構(gòu)和策略(英文版)(第6版)》新增內(nèi)容新增一章，介紹用于機(jī)器學(xué)習(xí)的隨機(jī)方法，包括一階貝葉斯網(wǎng)絡(luò)、各種隱馬爾可夫模型，馬爾可夫隨機(jī)域推理和循環(huán)信念傳播。介紹針對(duì)期望最大化學(xué)習(xí)以及利用馬爾可夫鏈蒙特卡羅采樣的結(jié)構(gòu)化學(xué)習(xí)的參數(shù)選擇，加強(qiáng)學(xué)習(xí)中馬爾可夫決策過(guò)程的利用。介紹智能體技術(shù)和本體的使用。介紹自然語(yǔ)言處理的動(dòng)態(tài)規(guī)劃（Earley語(yǔ)法析器），以及Viterbi等其他概率語(yǔ)法分析技術(shù)。書(shū)中的許多算法采用Prolog．Lisp和Java語(yǔ)言來(lái)構(gòu)建。

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