:人工智能:一种现代的方法拉塞尔,诺维格

基本信息

书名：人工智能:一种现代的方法(第3版)

定价：158.00元

作者：(美)拉塞尔,(美)诺维格

出版社：清华大学出版社

出版日期：2011-07-01

ISBN：9787302252955

字数：

页码：1132

版次：1

装帧：平装

开本：16开

商品重量：0.001kg

编辑推荐

    \n《人工智能(一种现代的方法第3版***》(作者拉塞尔、诺维格)是“大学计算机教育国外教材系列”之一，是高等院校本科生和研究生人工智能课的教材。全书仍分为八大部分：部分“人工智能”，第二部分“问题求解”，第三部分“知识与推理”，第四部分“规划”，第五部分“不确定知识与推理”，第六部分“学习”，第七部分“通信、感知与行动”，第八部分“结论”。\n《人工智能(一种现代的方法第3版***》适合于不同层次和领域的研究人员及学生。

内容提要

目录

I Artificial Intelligence1 Introduction 1.1 What Is AI? 1.2 The Foundations of Artificial Intelligence 1.3 The History of Artificial Intelligence 1.4 The State of the Art 1.5 Summary, Bibliographical and Historical Notes, Exercises 2 Intelligent Agents 2.1 Agents and Environments 2.2 Good Behavior: The Concept of Rationality 2.3 The Nature of Environments 2.4 The Structure of Agents 2.5 Summary, Bibliographical and Historical Notes, ExercisesII Problem-solving3 Solving Problems by Searching 3.1 Problem-Solving Agents 3.2 Example Problems r 3.3 Searching for Solutions 3.4 Uninformed Search Strategies 3.5 Informed (Heuristic) Search Strategies 3.6 Heuristic Functions 3.7 Summary, Bibliographical and Historical Notes, Exercises4 Beyond Classical Search 4.1 Local Search Algorithms and Optimization Problems 4.2 Local Search in Continuous Spaces 4.3 Searching with Nondeterministic Actions 4.4 Searching with Partial Observations 4.5 Online Search Agents and Unknown Environments 4.6 Summary, Bibliographical and Historical Notes, Exercises5 Adversariai Search 5.1 Games 5.2 Optimal Decisions in Games 5.3 Alpha-Beta Pruning 5.4 Imperfect Real-Time Decisions 5.5 Stochastic Games 5.6 Partially Observable Games 5.7 State-of-the-Art Game Programs 5.8 Alternative Approaches 5.9 Summary, Bibliographical and Historical Notes, Exercises6 Constraint Satisfaction Problems 6.1 Defining Constraint Satisfaction Problems 6.2 Constraint Propagation: Inference in CSPs 6.3 Backtracking Search for CSPs 6.4 Local Search for CSPs 6.5 The Structure of Problems 6.6 Summary, Bibliographical and Historical Notes, ExercisesIII Knowledge, reasoning, and planning7 Logical Agents 7.1 Knowledge-Based Agents 7.2 The Wumpus World 7.3 Logic 7.4 Propositional Logic: A Very Simple Logic 7.5 Propositional Theorem Proving 7.6 Effective Propositional Model Checking 7.7 Agents Based on Propositional Logic 7.8 Summary, Bibliographical and Historical Notes, Exercises8 First-Order Logic 8.1 Representation Revisited 8.2 Syntax and Semantics of First-Order Logic 8.3 Using First-Order Logic. 8.4 Knowledge Engineering in First-Order Logic 8.5 Summary, Bibliographical and Historical Notes, Exercises9 Inference in First-Order Logic 9.1 Propositional vs. First-Order Inference 9.2 Unification and Lifting 9.3 Forward Chaining 9.4 Backward Chaining 9.5 Resolution 9.6 Summary, Bibliographical and Historical Notes, Exer-cises10 Classical Planning 10.1 Definition of Classical Planning 10.2 Algorithms for Planning as State-Space Search 10.3 Planning Graphs 10.4 Other Classical Planning Approaches 10.5 Analysis of Planning Approaches 10.6 Summary, Bibliographical and Historical Notes, Exercises11 Planning and Acting in the Real World 11.1 Time,. Schedules, and Resources 11.2 Hierarchical Planning 11.3 Planning and Acting in Nondeterministic Domains 11.4 Multiagent Planning 11.5 Summary, Bibliographical and Historical Notes, Exercises12 Knowledge Representation 12.1 Ontological Engineering 12.2 Categories and Objects 12.3 Events 12.4 Mental Events and Ment.al Objects 12.5 Reasoning Systems for Categories 12.6 Reasoning with Default Information 12.7 The Inter Shopping World 12.8 Summary, Bibliographical and Historical Notes, ExercisesIV Uncertain knowledge and reasoning13 Quantifying Uncertainty 13.1 Acting under Uncertainty 13.2 Basic Probability Notation 13.3 Inference Using Full Joint Distributions 13.4 Independence 13.5 Bayes' Rule and Its Use 13.6 The Wumpus World Revisited 13.7 Summary, Bibliographical and Historical Notes, Exercises14 Probabilistic Reasoning 14.1 Representing Knowledge in an Uncertain Domain 14.2 The Semantics of Bayesian Networks 14.3 Efficient Representation of Conditional Distributions 14.4 Exact Inference in Bayesian Networks 14.5 Appromate Inference in Bayesian Networks 14.6 Relational and First-Order Probability Models 14.7 Other Approaches to Uncertain ReasOning 14.8 Summary, Bibliographical and Historical Notes, Exercises15 Probabilistic Reasoning over Time 15.1 Time and Uncertainty 15.2 Inference in Temporal Models 15.3 Hidden Markov Models 15.4 Kalman Filters 15.5 Dynamic Bayesian Networks 15.6 Keeping Track of Many Objects 15.7 Summary, Bibliographical and Historical Notes, Exercises16 Making Simple Decisions 16.1 Combining Beliefs and Desires under Uncertainty 16.2 The Basis of Utility Theory 16.3 Utility Functions 16.4 Multiattribute Utility Functions 16.5 Decision Networks 16.6 The Value of Information 16.7 Decision-Theoretic Expert Systems 16.8 Summary, Bibliographical and Historical Notes, Exercises17 Making Complex Decisions 17.1 Sequential Decision Problems 17.2 Value Iteration 17.3 Policy Iteration 17.4 Partially Observable MDPs 17.5 Decisions with Multiple Agents: Game Theory 17.6 Mechanism Design 17.7 Summary, Bibliographical and Historical Notes, ExercisesV Learning18 Learning from Examples 18.1 Forms of Learning 18.2 Supervised Learning 18.3 Learning Decision Trees 18.4 Evaluating and Choosing the Best Hypothesis 18.5 The Theory of Learning 18.6 Regression and:Classification with Linear Models 18.7 Artificial Neural Networks 18.8 Nonparametric Models 18.9 Support Vector Machines 18.10 Ensemble Learning 18. I 1 Practical Machine Learning 18.12 Summary, Bibliographical and Historical Notes, Exercises19 Knowledge in Learning 19.1 A Logical Formulation of Learning 19.2 Knowledge in Learning 19.3 Explanation-Based Learning 19.4 Learning Using Relevance Information 19.5 Inductive Logic Programming 19.6 Summary, Bibliographical and Historical Notes, Exercises20 Learning Probabilistic Models 20:1 Statistical Learning 20.2 Learning with Complete' Data 20.3 Learning with Hidden Variables: The EM Algorithm 20.4 Summary, Bibliographical and Historical Notes, Exercises21 Reinforcement Learning 21.1 Introduction 21.2 Passive Reinforcement Learning 21.3 Active Reinforcement Learning 21.4 Generalization in Reinforcement Learning 21.5 Policy Searcti 21.6 Applications of Reinforcement Learning 21.7 Summary, Bibliographical and Historical Notes, ExercisesVI Communicating, perceiving, and acting22 Natural Language Pi'ocessing 22.1 Language Models 22.2 Text Classification 22.3 Information Retrieval 22.4 Information Extraction 22.5 Summary, Bibliographical and Historical Notes, Exercises23 Natural Language for Communication 23.1 Phrase Structure Grammars 23.2 Syntactic Analysis (Parsing) 23.3 Augmented Grammars and Semantic Interpretation 23.4 Machine Translation 23.5 Speech Recognition 23.6 Summary, Bibliographical and Historical Notes, Exercises24 Perception 24.1 Image Formation 24.2 Early Image-Processing Operations 24.3 Object Recognition by Appearance 24.4 Reconstructing the3D World 24.5 Object Recognition from Structural Information 24.6 .Using Vision 24.7 Summary, Bibliographical and Histiarical Notes, Exercises25 Robotics 25.1 Introduction 25.2 Robot Hardware 25.3 Robotic Perception 25.4 Planning to Move 25.5 Planning Uncertain Movements 25.6 Moving 25.7 Robotic Software Architectures 25.8 Application Domains . 25.9 Summary, Bibliographical and Historical Notes, Exercises VII Conclusions26 Philosophical Foundations 26.1 Weak AI: Can Machines Act Intelligently? 26.2 Strong AI: Can Machines Really Think? 26.3 The Ethics and Risks of Developing Artificial Intelligence 26.4 Summary, Bibliographical and Historical Notes, Exercises27 AI: The Present and Future 27.1 Agent Components 27.2 Agent Architectures 27.3 Are We Going in the Right Direction? 27.4 What If AI Does Succeed? A Mathematical background A. 1 Complety Analysis and O0 Notation A.2 Vectors, Matrices, and Linear Algebra A.3 Probability DistributionsB Notes on Languages and Algorithms B.1 Defining Languages with Backus-Naur Form (BNF) B.2 Describing Algorithms with Pseudocode B.3 Online HelpBibliographyIndex

作者介绍

Stuart Russell，1962年生于英格兰的Portsmouth。他于1982年以一等成绩在牛津大学获得物理学学士学位，并于1986年在斯坦福大学获得计算机科学的博士学位。之后他进入加州大学伯克利分校，任计算机科学教授，智能系统中心主任，拥有Smith-Zadeh工程学讲座教授头衔。1990年他获得国家科学基金的“总统青年研究者奖”（Presidential Young Investigator Award），1995年他是“计算机与思维奖”（Computer and Thought Award）的获得者之一。1996年他是加州大学的Miller教授（Miller Professor），并于2000年被任命为首席讲座教授（Chancellor's Professorship）。1998年他在斯坦福大学做过Forsythe纪念演讲（Forsythe Memorial Lecture）。他是美国人工智能学会的会士和前执行委员会委员。他已经发表100多篇论文，主题广泛涉及人工智能领域。他的其他著作包括《在类比与归纳中使用知识》（The Use of Knowledge in Analogy abd Induction）．以及（与Eric Wefald合著的）《做正确的事情：有限理性的研究》（Do the Right Thing: Studies in Limited Rationality）。

Peter Norvig，现为Google研究院主管（Director of Research），2002-2005年为负责核心Web搜索算法的主管。他是美国人工智能学会的会士和ACM的会士。他曾经是NASAAmes研究中心计算科学部的主任，负责NASA在人工智能和机器人学领域的研究与开发，他作为Junglee的首席科学家帮助开发了一种早的互联息抽取服务。他在布朗（ Brown）大学得应用数学学士学位，在加州大学伯克利分校获得计算机科学的博士学位。他获得了伯克利“校友和工程创新奖”，从NASA获得了“非凡成就勋章”。他曾任南加州大学的教授，并是伯克利的研究员。他的其他著作包括《人工智能程序设计范型：通用Lisp语言的案例研究》（Paradigms of AI Programming: Case Studies in Common Lisp）和《Verbmobil：一个面对面对话的翻译系统》（Verbmobil:A Translation System for Face-to-FaceDialog），以及《UNIX的智能帮助系统》（lntelligent Help Systemsfor UNIX）。

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