人工智能

內(nèi)容概要

《人工智能(一種現(xiàn)代的方法第3版影印版》(作者拉塞爾、諾維格)是最權(quán)威、最經(jīng)典的人工智能教材，已被全世界100多個(gè)國家的1200多所大學(xué)用作教材。
《人工智能(一種現(xiàn)代的方法第3版影印版》的最新版全面而系統(tǒng)地介紹了人工智能的理論和實(shí)踐，闡述了人工智能領(lǐng)域的核心內(nèi)容，并深入介紹了各個(gè)主要的研究方向。全書仍分為八大部分：第一部分“人工智能”，第二部分“問題求解”，第三部分“知識(shí)與推理”，第四部分“規(guī)劃”，第五部分“不確定知識(shí)與推理”，第六部分“學(xué)習(xí)”，第七部分“通信、感知與行動(dòng)”，第八部分“結(jié)論”?！度斯ぶ悄?一種現(xiàn)代的方法第3版影印版》既詳細(xì)介紹了人工智能的基本概念、思想和算法，還描述了其各個(gè)研究方向最前沿的進(jìn)展，同時(shí)收集整理了詳實(shí)的歷史文獻(xiàn)與事件。另外，《人工智能(一種現(xiàn)代的方法第3版影印版》的配套網(wǎng)址為教師和學(xué)生提供了大量教學(xué)和學(xué)習(xí)資料。
《人工智能(一種現(xiàn)代的方法第3版影印版》適合于不同層次和領(lǐng)域的研究人員及學(xué)生，是高等院校本科生和研究生人工智能課的首選教材，也是相關(guān)領(lǐng)域的科研與工程技術(shù)人員的重要參考書。

作者簡介

作者：（美國）拉塞爾（Stuart J.Russell） （美國）諾維格（Peter Norvig）

書籍目錄

I Artificial Intelligence
1 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, Exercises
II Problem-solving
3 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, Exercises
4 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, Exercises
5 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, Exercises
6 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, Exercises
III Knowledge, reasoning, and planning
7 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, Exercises
8 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, Exercises
9 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-cises
10 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, Exercises
11 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, Exercises
12 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 Internet Shopping World 
 12.8 Summary, Bibliographical and Historical Notes, Exercises
IV Uncertain knowledge and reasoning
13 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, Exercises
14 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 Approximate 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, Exercises
15 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, Exercises
16 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, Exercises
17 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, Exercises
V Learning
18 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, Exercises
19 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, Exercises
20 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, Exercises
21 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, Exercises
VI Communicating, perceiving, and acting
22 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, Exercises
23 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, Exercises
24 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, Exercises
25 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 Conclusions
26 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, Exercises
27 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 Complexity Analysis and O0 Notation
 A.2 Vectors, Matrices, and Linear Algebra 
 A.3 Probability Distributions
B Notes on Languages and Algorithms
 B.1 Defining Languages with Backus-Naur Form (BNF)
 B.2 Describing Algorithms with Pseudocode 
 B.3 Online Help
Bibliography
Index

章節(jié)摘錄

版權(quán)頁：插圖：The last component of the learning agent is the problem generator. It is responsible for suggesting actions that will lead to new and informative experiences. The point is that if the performance element had its way, it would keep doing the actions that are best, given what it knows. But if the agent is willing to explore a little and do some perhaps suboptimal actions in the short run, it might discover much better actions for the long run. The problem generator's job is to suggest these exploratory actions. This is what scientists do when they carry out experiments. Galileo did not think that dropping rocks from the top of a tower in Pisa was valuable in itself. He was not trying to break the rocks or to modify the brains of unfortunate passers-by. His aim was to modify his own brain by identifying a better theory of the motion of objects.To make the overall design more concrete, let us return to the automated taxi example. The performance element consists of whatever collection of knowledge and procedures the taxi has for selecting its driving actions. The taxi goes out on the road and drives, using this performance element. The critic observes the world and passes information along to the learning element. For example, after the taxi makes a quick left turn across three lanes of traffic, the critic observes the shocking language used by other drivers. From this experience, the learning element is able to formulate a rule saying this was a bad action, and the performance element is modified by installation of the new rule. The problem generator might identify certain areas of behavior in need of improvement and suggest experiments, such as trying out the brakes on different road surfaces under different conditions.The learning element can make changes to any of the "knowledge" components shown in the agent diagrams (Figures 2.9, 2.11, 2.13, and 2.14). The simplest cases involve learning directly from the percept sequence. Observation of pairs of successive states of the environment can allow the agent to learn "How the world evolves," and observation of the results of its actions can allow the agent to learn "What my actions do." For example, if the taxi exerts a certain braking pressure when driving on a wet road, then it will soon find out how much deceleration is actually achieved. Clearly, these two learning tasks are more difficult if the environment is only partially observable.

編輯推薦

《人工智能:一種現(xiàn)代的方法(第3版)(影印版)》為大學(xué)計(jì)算機(jī)教育國外著名教材系列之一。

圖書封面

圖書標(biāo)簽Tags

無

評(píng)論、評(píng)分、閱讀與下載

---

    人工智能 PDF格式下載

---