Artificial Intelligence: A Modern Approach, 3/e (GE-Paperback)
內容描述
Description
For one or two-semester, undergraduate or graduate-level courses in Artificial Intelligence.
The long-anticipated revision of this best-selling text offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence.
Features
Nontechnical learning material.
Provides a simple overview of major concepts, uses a nontechnical language to help increase understanding. Makes the book accessible to a broader range of students.
The Internet as a sample application for intelligent systems — Examples of logical reasoning, planning, and natural language processing using Internet agents.
Promotes student interest with interesting, relevant exercises.
Increased coverage of material — New or expanded coverage of constraint satisfaction, local search planning methods, multi-agent systems, game theory, statistical natural language processing and uncertain reasoning over time. More detailed descriptions of algorithms for probabilistic inference, fast propositional inference, probabilistic learning approaches including EM, and other topics.
Brings students up to date on the latest technologies, and presents concepts in a more unified manner.
Updated and expanded exercises — 30% of the exercises are revised or NEW.
More Online Software.
Allows many more opportunities for student projects on the web.
A unified, agent-based approach to AI — Organizes the material around the task of building intelligent agents.
Shows students how the various subfields of AI fit together to build actual, useful programs.
Comprehensive, up-to-date coverage — Includes a unified view of the field organized around the rational decision making paradigm.
A flexible format.
Makes the text adaptable for varying instructors' preferences.
In-depth coverage of basic and advanced topics.
Provides students with a basic understanding of the frontiers of AI without compromising complexity and depth.
Pseudo-code versions of the major AI algorithms are presented in a uniform fashion, and Actual Common Lisp and Python implementations of the presented algorithms are available via the Internet.
Gives instructors and students a choice of projects; reading and running the code increases understanding.
Author Maintained Website
Visit http://aima.cs.berkeley.edu/ to access text-related Comments and Discussions, AI Resources on the Web, and Online Code Repository, Instructor Resources, and more!
New to this Edition
This edition captures the changes in AI that have taken place since the last edition in 2003. There have been important applications of AI technology, such as the widespread deployment of practical speech recognition, machine translation, autonomous vehicles, and household robotics. There have been algorithmic landmarks, such as the solution of the game of checkers. And there has been a great deal of theoretical progress, particularly in areas such as probabilistic reasoning, machine learning, and computer vision. Most important from the authors' point of view is the continued evolution in how we think about the field, and thus how the book is organized. The major changes are as follows:
More emphasis is placed on partially observable and nondeterministic environments, especially in the nonprobabilistic settings of search and planning. The concepts of belief state (a set of possible worlds) and state estimation (maintaining the belief state) are introduced in these settings; later in the book, probabilities are added.
In addition to discussing the types of environments and types of agents, there is more in more depth coverage of the types of representations that an agent can use. Differences between atomic representations (in which each state of the world is treated as a black box), factored representations (in which a state is a set of attribute/value pairs), and structured representations (in which the world consists of objects and relations between them) are distinguished.
Coverage of planning goes into more depth on contingent planning in partially observable environments and includes a new approach to hierarchical planning.
New material on first-order probabilistic models is added, including open-universe models for cases where there is uncertainty as to what objects exist.
The introductory machine-learning chapter is completely rewritten, stressing a wider variety of more modern learning algorithms and placing them on a firmer theoretical footing.
Expanded coverage of Web search and information extraction, and of techniques for learning from very large data sets.
20% of the citations in this edition are to works published after 2003.
Approximately 20% of the material is brand new. The remaining 80% reflects older work but is largely rewritten to present a more unified picture of the field.
目錄大綱
Table of Contents
I. Artificial Intelligence
- 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 - 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 - Solving Problems by Searching
3.1 Problem-Solving Agents
3.2 Example Problems
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 - 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 - Adversarial 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 - 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 - 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 - 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 - 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, Exercises - 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 - 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 Mental 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 - 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 - 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 - 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 - 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 - 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 - 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
18.11 Practical Machine Learning
18.12 Summary, Bibliographical and Historical Notes, Exercises - 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 - 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 - Reinforcement Learning
21.1 Introduction
21.2 Passive Reinforcement Learning
21.3 Active Reinforcement Learning
21.4 Generalization in Reinforcement Learning
21.5 Policy Search
21.6 Applications of Reinforcement Learning
21.7 Summary, Bibliographical and Historical Notes, Exercises
VI. Communicating, Perceiving, and Acting - Natural Language Processing
22.1 Language Models
22.2 Text Classification
22.3 Information Retrieval
22.4 Information Extraction
22.5 Summary, Bibliographical and Historical Notes, Exercises - 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 - Perception
24.1 Image Formation
24.2 Early Image-Processing Operations
24.3 Object Recognition by Appearance
24.4 Reconstructing the 3D World
24.5 Object Recognition from Structural Information
24.6 Using Vision
24.7 Summary, Bibliographical and Historical Notes, Exercises - 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 - 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?
Appendices
A. Mathematical Background
A.1 Complexity Analysis and O() 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
