目次

1. Introduction to Artificial Intelligence 1.1 History of Artificial Intelligence 1.2 Outline of this Book Part I LOGICAL INTELLIGENCE 2. Propositional Logic 2.1 Basics of Propositional Logic 2.2 Resolution 2.3 Artificial Intelligence Applications 2.4 Discussion and Further Reading 3. First-Order Logic 3.1 Basics of First-Order Logic 3.2 Artificial Intelligence Applications 3.3 Discussion and Further Reading 4. Certain Knowledge Representation 4.1 Taxonomic Knowledge 4.2 Frames 4.3 Nonmonotonic Logic 4.4 Discussion and Further Reading 5. Learning Deterministic Models 5.1 Supervised Learning 5.2 Regression 5.3 Parameter Estimation 5.4 Learning a Decision Tree PART II PROBABILISTIC INTELLIGENCE 6. Probability 6.1 Probability Basics 6.2 RandomVariables 6.3 Meaning of Probability 6.4 RandomVariables in Applications 6.5 Probability in the Wumpus World 7. Uncertain Knowledge Representation 7.1 Intuitive Introduction to Bayesian Networks 7.2 Properties of Bayesian Networks 7.3 Causal Networks as Bayesian Networks 7.4 Inference in Bayesian Networks 7.5 Networks with Continuous Variables 7.6 Obtaining the Probabilities 7.7 Large-Scale Application: Promedas 8. Advanced Properties of Bayesian Network 8.1 Entailed Conditional Independencies 8.2 Faithfulness 8.3 Markov Equivalence 8.4 Markov Blankets and Boundaries 9. Decision Analysis 9.1 Decision Trees 9.2 Influence Diagrams 9.3 Modeling Risk Preferences 9.4 Analyzing Risk Directly 9.5 Good Decision versus Good Outcome 9.6 Sensitivity Analysis 9.7 Value of Information 9.8 Discussion and Further Reading 10. Learning Probabilistic Model Parameters 10.1 Learning a Single Parameter 10.2 Learning Parameters in a Bayesian Network . 10.3 Learning Parameters with Missing Data 11. Learning Probabilistic Model Structure 11.1 Structure Learning Problem 11.2 Score-Based Structure Learning 11.3 Constraint-Based Structure Learning 11.4 Application: MENTOR 11.5 Software Packages for Learning 11.6 Causal Learning 11.7 Class Probability Trees 11.8 Discussion and Further Reading 12. Unsupervised Learning and Reinforcement Learning 12.1 Unsupervised Learning 12.2 Reinforcement Learning12.3 Discussion and Further Reading PART III EMERGENT INTELLIGENCE 13. Evolutionary Computation 13.1 Genetics Review 13.2 Genetic Algorithms 13.3 Genetic Programming13.4 Discussion and Further Reading 14. Swarm Intelligence 14.1 Ant System 14.2 Flocks 14.3 Discussion and Further Reading PART IV NEURAL INTELLIGENCE 15. Neural Networks and Deep Learning 15.1 The Perceptron 15.2 Feedforward Neural Networks 15.3 Activation Functions 15.4 Application to Image Recognition 15.5 Discussion and Further Reading PART V LANGUAGE UNDERSTANDING 16. Natural Language Understanding 16.1 Parsing 16.2 Semantic Interpretation 16.3 Concept/Knowledge Interpretation 16.4 Information Extraction 16.5 Discussion and Further Reading