人工智能（英文版）

內(nèi)容概要

　 本書(shū)介紹了人工智能領(lǐng)域中最重要的一個(gè)概念——智能代理。本書(shū)從最基本的反應(yīng)式代理入手，逐步向人們展示了現(xiàn)代人工智能不斷增強(qiáng)的認(rèn)知能力，同時(shí)也例證了該領(lǐng)域中重要且經(jīng)久不衰的思維、思想。神經(jīng)網(wǎng)絡(luò)、遺傳程序設(shè)計(jì)、計(jì)算機(jī)視覺(jué)、探視搜索、知識(shí)表示和推理、貝葉斯網(wǎng)絡(luò)、規(guī)劃和語(yǔ)言理解等有關(guān)人工智能的重要內(nèi)容都通過(guò)本書(shū)所描述的各種代理的不斷增長(zhǎng)的能力得以展現(xiàn)。本書(shū)作者是人工智能領(lǐng)域的主要開(kāi)創(chuàng)者和重要帶頭人，正是他提供給了廣大讀者一個(gè)耳目一新和富有生機(jī)的合成技術(shù)，該技術(shù)將領(lǐng)導(dǎo)人類把整個(gè)人工智能領(lǐng)域的研究引向一個(gè)新的境界。 Nils J. Nilsson: Artificial Intelligence, A New Synthesis. Copyright @ 1998 by Morgan Kaufmann Publishers, Inc. Harcourt Asia Pte Ltd under special arrangement with Morgan Kaufmann authorizes China Machine Press to print and exclusively distribute this edition, which is the only authorized complete and unabridged reproduction of the latest American Edition published and priced for sale in China only, not including Hong Kong SAR and Taiwan. Unauthorized export of this edition is a violation of the Copyright Act. Violation of this Law is subjected to Civil and Criminal penalties.

作者簡(jiǎn)介

About the author
Nils J. Nilsson's long and rich research career has contributed much to Al. His previous books,
considered classics in the field, include Learning Machines, Problem-Solving Methods in Artificial
Intelligence, Logical Foundations of Artificial Intelligence, and Principles of Artificial Intelligence.
Dr. Nilsson is Kumagai Professor of Engineering, Emeritus, at Stanford University. He has served
on the editorial boards of Artificial Intelligence and Machine Learning and as an area editor for the
Joumal of the Association for Computing Machinery. Former chairman of the Department of
Computer Science at Stanford and Former Director of the SRl Artificial Intelligence Center, he is
also a past president and fellow of the American Association for Artificial Intelligence.

書(shū)籍目錄

Preface1 Introduction 1.1 What is AI?  1.2 Approaches to Artificial Intelligence  1.3 Brief History of AI  1.4 Plan of the Book  1.5 Additional Readings and Discussion I Reactive Machines 2 Stimulus-Response Agents 2.1 Perception and Action  2.2 Representing and Implementing Action Functions  2.3 Additional Readings and Discussion 3 Neural Networks  3.1 Introduction  3.2 Training Single TLUs  3.3 Neural Networks  3.4 Generalization, Accuracy, and Overfitting  3.5 Additional Readings and Discussion 4 Machine Evolution  4.1 Evolutionary Computation  4.2 Genetic Programming  4.3 Additional Readings and Discussion 5 State Machines  5.1 Representing the Environment by Feature Vectors  5.2 Elman Networks  5.3 Iconic Representations  5.4 Blackboard Systems  5.5 Additional Readings and Discussion 6 Robot Vision  6.1 Introduction  6.2 Steering a Van  6.3 Two Stages of Robot Vision  6.4 Image Processing  6.5 Scene Analysis  6.6 Stereo Vision  6.7 Additional Readings and Discussion II Search in State Spaces7 Agents that Plan  7.1 Memory Versus Computation  7.2 State-Space Graphs  7.3 Searching Explicit State Spaces  7.4 Feature-Based State Spaces  7.5 Graph Notation 7.6 Additional Readings and Discussion 8 Uninformed Search  8.1 Formulating the State Space  8.2 Components of Implicit State-Space Graphs  8.3 Breadth-First Search  8.4 Depth-First or Bracktracking Search  8.5 Iterative Deepening  8.6 Additional Readings and Discussion 9 Heuristic Search  9.1 Using Evaluation Functions  9.2 A General Graph-Searching Algorithm  9.3 Heuristic Functions and Search Efficiency  9.4 Additional Readings and Discussion 10 Planning, Acting, and Learning  10.1 The Sense/Plan/Act Cycle  10.2 Approximate Search  10.3 Learning Heuristic Functions  10.4 Rewards Instead of Goals  10.5 Additional Readings and Discussion 11 Alternative Search Formulations and Applications  11.1 Assignment Problems  11.2 Constructive Methods  11.3 Heuristic Repair  11.4 Function Optimization 12 Adversarial Search  12.1 Two-Agent Games  12.2 The Minimax Procedure  12.3 The Alpha-Beta Procedure  12.4 The Search Efficiency of the Alpha-Beta Procedure  12.5 Other Important Matters  12.6 Games of Chance  12.7 Learning Evaluation Functions  12.8 Additional Readings and Discussion III Knowledge Representation and Reasoning13 The Propositional Calculus  13.1 Using Constraints on Feature Values  13.2 The Language  13.3 Rules of Inference  13.4 Definition of Proof  13.5 Semantics  13.6 Soundness and Completeness  13.7 The PSAT Problem  13.8 Other Important Topics 14 Resolution in The Propositional Calculus  14.1 A New Rule of Inference: Resolution  14.2 Converting Arbitrary wffs to Conjunctions of Clauses  14.3 Resolution Refutations  14.4 Resolution Refutation Search Strategies  14.5 Horn Clauses 15 The Predicate Calculus  15.1 Motivation  15.2 The Language and its Syntax  15.3 Semantics  15.4 Quantification  15.5 Semantics of Quantifiers  15.6 Predicate Calculus as a Language for Representing Knowledge  15.7 Additional Readings and Discussion 16 Resolution in the Predicate Calculus  16.1 Unification  16.2 Predicate-Calculus Resolution  16.3 Completeness and Soundness  16.4 Converting Arbitrary wffs to Clause Form  16.5 Using Resolution to Prove Theorems  16.6 Answer Extraction  16.7 The Equality Predicate  16.8 Additional Readings and Discussion 17 Knowledge-Based Systems  17.1 Confronting the Real World  17.2 Reasoning Using Horn Clauses  17.3 Maintenance in Dynamic Knowledge Bases  17.4 Rule-Based Expert Systems  17.5 Rule Learning  17.6 Additional Readings and Discussion 18 Representing Commonsense Knowledge  18.1 The Commonsense World  18.2 Time  18.3 Knowledge Representation by Networks  18.4 Additional Readings and Discussion 19 Reasoning with Uncertain Information  19.1 Review of Probability Theory  19.2 Probabilistic Inference  19.3 Bayes Networks  19.4 Patterns of Inference in Bayes Networks  19.5 Uncertain Evidence  19.6 D-Seperation  19.7 Probabilistic Inference in Polytrees  19.8 Additional Readings and Discussion 20 Learning and Acting with Bayes Nets  20.1 Learning Bayes Nets  20.2 Probabilistic Inference and Action  20.3 Additional Readings and Discussion IV Planning Method Based on Logic21 The Situation Calculus  21.1 Reasoning about States and Actions  21.2 Some Difficulties  21.3 Generating Plans  21.4 Additional Reading and Discussion 22 Planning  22.1 STRIPS Planning Systems  22.2 Plan Spaces and Partial-Order Planning  22.3 Hierarchical Planning  22.4 Learning Plans'  22.5 Additional Readings and Discussion V Communication and Integration23 Multiple Agents  23.1 Interacting Agents  23.2 Models of Other Agents  23.3 A Modal Logic of Knowledge  23.4 Additional Readings and Discussion 24 Communication Among Agents  24.1 Speech Acts  24.2 Understanding Language Strings  24.3 Efficient Communication  24.4 Natural Language Processing  24.5 Additional Readings and Discussion 25 Agent Architectures  25.1 Three-Level Architectures  25.2 Goal Arbitration  25.3 The Triple-Tower Architecture  25.4 Bootstrapping  25.5 Additional Readings and Discussion

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