人工智能：復雜問題求解的結構和策略（英文版）（第6版） 下載 mobi epub pdf 電子書 2024

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　　《人工智能：復雜問題求解的結構和策略（英文版）（第6版）》是一本經典的人工智能教材，全麵闡述瞭人工智能的基礎理論，有效結閤瞭求解智能問題的數據結構以及實現的算法，把人工智能的應用程序應用於實際環境中，並從社會和哲學、心理學以及神經生理學角度對人工智能進行瞭獨特的討論。《人工智能：復雜問題求解的結構和策略（英文版）（第6版）》新增內容新增一章，介紹用於機器學習的隨機方法，包括一階貝葉斯網絡、各種隱馬爾可夫模型，馬爾可夫隨機域推理和循環信念傳播。
　　介紹針對期望大化學習以及利用馬爾可夫鏈濛特卡羅采

內容簡介

　　《人工智能：復雜問題求解的結構和策略（英文版）（第6版）》英文影印版由PearsonEducationAsiaLtd授權機械工業齣版社少數齣版。未經齣版者書麵許可，不得以任何方式復製或抄襲《人工智能：復雜問題求解的結構和策略（英文版）（第6版）》內容。
　　僅限於中華人民共和國境內（不包括中國香港、澳門特彆行政區和中國颱灣地區）銷售發行。
　　《人工智能：復雜問題求解的結構和策略（英文版）（第6版）》封麵貼有PearsonEducation（培生教育齣版集團）激光防僞標簽，無標簽者不得銷售。

作者簡介

　　George F.Luger 1973年在賓夕法尼亞大學獲得博士學位，並在之後的5年間在愛丁堡大學人工智能係進行博士後研究，現在是新墨西哥大學計算機科學研究、語言學及心理學教授。

內頁插圖

精彩書評

　　“在該領域裏學生經常遇到許羅很難的概念，通過深刻的實例與簡單明瞭的祝圈，該書清晰而準確塏闞述瞭這些概念。”
　　——Toseph Lewis，聖迭戈州立大學
　　“本書是人工智能課程的完美補充。它既給讀者以曆史的現點，又給幽所有莰術的賓用指南。這是一本必須要推薦的人工智能的田書。”
　　——-Pascal Rebreyend，瑞典達拉那大學
　　“該書的寫作風格和全麵的論述使它成為人工智能領域很有價值的文獻。”
　　——Malachy Eat

目錄

Preface
Publishers 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 BayesTheorem
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
7.2 Conceptual Graphs：A Network Language
7.3 Alternative Representations and Ontologies
7.4 Agent Based and Distributed Problem Solving
7.5 Epilogue and References
7.6 Exercises

8 STRONG METHOD PROBLEM SOLVING
8.0 Introduction
8.1 Overview ofExpert Sygem Technology
8.2 Rule.Based Expert Sygems
8.3 Model-Based,Case Based and Hybrid Systems
8.4 Planning
8.5 Epilogue and References
8.6 Exercises
9 REASONING IN UNCERTAIN STUATIONS
9.0 Introduction
9.1 Logic-Based Abductive Inference
9.2 Abduction：Alternatives to Logic
9.3 The Stochastic Approach to Uncertainty
9.4 Epilogue and References
9.5 Exercises

PART Ⅳ
MACHINE LEARNING
10 MACHINE LEARNING：SYMBOL-BASED
10.0 Introduction
10.1 A Framework for Symbol based Learning
10.2 version Space Search
10.3 The ID3 Decision Tree Induction Algorithm
10.4 Inductive Bias and Learnability
10.5 Knowledge and Learning
10.6 Unsupervised Learning
10.7 Reinforcement Learning
10.8 Epilogue and Referenees
10.9 Exercises

11 MACHINE LEARNING：CONNECTIONtST
11.0 Introduction
11.1 Foundations for Connectionist Networks
11.2 Perceptron Learning
11.3 Backpropagation Learning
11.4 Competitive Learning
11.5 Hebbian Coincidence Learning
11.6 Attractor Networks or“Memories”
11.7 Epilogue and References
11.8 Exercises 506

12 MACHINE LEARNING：GENETIC AND EMERGENT
12.0 Genetic and Emergent MedeIs ofLearning
12.1 11Ic Genetic Algorithm
12.2 Classifier Systems and Genetic Programming
12.3 Artmcial Life and Society-Based Learning
12.4 EpilogueandReferences
12.5 Exercises

13 MACHINE LEARNING：PROBABILISTIC
13.0 Stochastic andDynamicModelsofLearning
13.1 Hidden Markov Models(HMMs)
13.2 DynamicBayesianNetworksandLearning
13.3 Stochastic Extensions to Reinforcement Learning
13.4 EpilogueandReferences
13.5 Exercises

PART Ⅴ
AD，ANCED TOPlCS FOR Al PROBLEM SOLVING
14 AUTOMATED REASONING
14.0 Introduction to Weak Methods inTheorem Proving
14.1 TIIeGeneralProblem SolverandDifiel"enceTables
14.2 Resolution TheOrem Proving
14.3 PROLOG and Automated Reasoning
14.4 Further Issues in Automated Reasoning
14.5 EpilogueandReferences
14.6 Exercises

15 UNDERs-rANDING NATURAL LANGUAGE
15.0 TheNaturalLang~~geUnderstandingProblem
15.1 Deconstructing Language：An Analysis
15.2 Syntax
15.3 TransitionNetworkParsers and Semantics
15.4 StochasticTools forLanguage Understanding
15.5 Natural LanguageApplications
15.6 Epilogue and References
15.7 Exercises
……
PART Ⅵ EPILOGUE
16 ARTIFICIAL INTELLIGENCE AS EMPIRICAL ENQUIRY

精彩書摘

　　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 tablespurpose 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.

前言/序言

　　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. 人工智能：復雜問題求解的結構和策略（英文版）（第6版） 下載 mobi epub pdf txt 電子書 格式

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