Artificial intelligence:a modern approach

　　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