Artificial intelligence : A modern approach

Table of Contents:
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;
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. 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;
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, Exercises;
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 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;
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;
18.11 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 Search;
21.6 Applications of Reinforcement Learning;
21.7 Summary, Bibliographical and Historical Notes, Exercises;
VI. Communicating, Perceiving, and Acting;
22. 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;
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 the 3D World;
24.5 Object Recognition from Structural Information;
24.6 Using Vision;
24.7 Summary, Bibliographical and Historical 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?;
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.