Artificial Intelligence: A Modern Approach

Preface
Contents
Part I: Artificial Intelligence
Chapter 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 Risks and Benefits of AI
Summary
Bibliographical and Historical Notes
Chapter 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
Summary
Bibliographical and Historical Notes
Part II: Problem-solving
Chapter 3 Solving Problems by Searching
3.1 Problem-Solving Agents
3.2 Example Problems
3.3 Search Algorithms
3.4 Uninformed Search Strategies
3.5 Informed (Heuristic) Search Strategies
3.6 Heuristic Functions
Summary
Bibliographical and Historical Notes
Chapter 4 Search in Complex Environments
4.1 Local Search and Optimization Problems
4.2 Local Search in Continuous Spaces
4.3 Search with Nondeterministic Actions
4.4 Search in Partially Observable Environments
4.5 Online Search Agents and Unknown Environments
Summary
Bibliographical and Historical Notes
Chapter 5 Adversarial Search and Games
5.1 Game Theory
5.2 Optimal Decisions in Games
5.3 Heuristic Alpha--Beta Tree Search
5.4 Monte Carlo Tree Search
5.5 Stochastic Games
5.6 Partially Observable Games
5.7 Limitations of Game Search Algorithms
Summary
Bibliographical and Historical Notes
Chapter 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
Summary
Bibliographical and Historical Notes
Part III: Knowledge, reasoning, and planning
Chapter 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
Summary
Bibliographical and Historical Notes
Chapter 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
Summary
Bibliographical and Historical Notes
Chapter 9 Inference in First-Order Logic
9.1 Propositional vs. First-Order Inference
9.2 Unification and First-Order Inference
9.3 Forward Chaining
9.4 Backward Chaining
9.5 Resolution
Summary
Bibliographical and Historical Notes
Chapter 10 Knowledge Representation
10.1 Ontological Engineering
10.2 Categories and Objects
10.3 Events
10.4 Mental Objects and Modal Logic
10.5 Reasoning Systems for Categories
10.6 Reasoning with Default Information
Summary
Bibliographical and Historical Notes
Chapter 11 Automated Planning
11.1 Definition of Classical Planning
11.2 Algorithms for Classical Planning
11.3 Heuristics for Planning
11.4 Hierarchical Planning
11.5 Planning and Acting in Nondeterministic Domains
11.6 Time, Schedules, and Resources
11.7 Analysis of Planning Approaches
Summary
Bibliographical and Historical Notes
Part IV: Uncertain knowledge and reasoning
Chapter 12 Quantifying Uncertainty
12.1 Acting under Uncertainty
12.2 Basic Probability Notation
12.3 Inference Using Full Joint Distributions
12.4 Independence
12.5 Bayes' Rule and Its Use
12.6 Naive Bayes Models
12.7 The Wumpus World Revisited
Summary
Bibliographical and Historical Notes
Chapter 13 Probabilistic Reasoning
13.1 Representing Knowledge in an Uncertain Domain
13.2 The Semantics of Bayesian Networks
13.3 Exact Inference in Bayesian Networks
13.4 Approximate Inference for Bayesian Networks
13.5 Causal Networks
Summary
Bibliographical and Historical Notes
Chapter 14 Probabilistic Reasoning over Time
14.1 Time and Uncertainty
14.2 Inference in Temporal Models
14.3 Hidden Markov Models
14.4 Kalman Filters
14.5 Dynamic Bayesian Networks
Summary
Bibliographical and Historical Notes
Chapter 15 Probabilistic Programming
15.1 Relational Probability Models
15.2 Open-Universe Probability Models
15.3 Keeping Track of a Complex World
15.4 Programs as Probability Models
Summary
Bibliographical and Historical Notes
Chapter 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 Unknown Preferences
Summary
Bibliographical and Historical Notes
Chapter 17 Making Complex Decisions
17.1 Sequential Decision Problems
17.2 Algorithms for MDPs
17.3 Bandit Problems
17.4 Partially Observable MDPs
17.5 Algorithms for Solving POMDPs
Summary
Bibliographical and Historical Notes
Chapter 18 Multiagent Decision Making
18.1 Properties of Multiagent Environments
18.2 Non-Cooperative Game Theory
18.3 Cooperative Game Theory
18.4 Making Collective Decisions
Summary
Bibliographical and Historical Notes
Part V: Machine Learning
Chapter 19 Learning from Examples
19.1 Forms of Learning
19.2 Supervised Learning
19.3 Learning Decision Trees
19.4 Model Selection and Optimization
19.5 The Theory of Learning
19.6 Linear Regression and Classification
19.7 Nonparametric Models
19.8 Ensemble Learning
19.9 Developing Machine Learning Systems
Summary
Bibliographical and Historical Notes
Chapter 20 Learning Probabilistic Models
20.1 Statistical Learning
20.2 Learning with Complete Data
20.3 Learning with Hidden Variables: The EM Algorithm
Summary
Bibliographical and Historical Notes
Chapter 21 Deep Learning
21.1 Simple Feedforward Networks
21.2 Computation Graphs for Deep Learning
21.3 Convolutional Networks
21.4 Learning Algorithms
21.5 Generalization
21.6 Recurrent Neural Networks
21.7 Unsupervised Learning and Transfer Learning
21.8 Applications
Summary
Bibliographical and Historical Notes
Chapter 22 Reinforcement Learning
22.1 Learning from Rewards
22.2 Passive Reinforcement Learning
22.3 Active Reinforcement Learning
22.4 Generalization in Reinforcement Learning
22.5 Policy Search
22.6 Apprenticeship and Inverse Reinforcement Learning
22.7 Applications of Reinforcement Learning
Summary
Bibliographical and Historical Notes
Part VI: Communicating, perceiving, and acting
Chapter 23 Natural Language Processing
23.1 Language Models
23.2 Grammar
23.3 Parsing
23.4 Augmented Grammars
23.5 Complications of Real Natural Language
23.6 Natural Language Tasks
Summary
Bibliographical and Historical Notes
Chapter 24 Deep Learning for Natural Language Processing
24.1 Word Embeddings
24.2 Recurrent Neural Networks for NLP
24.3 Sequence-to-Sequence Models
24.4 The Transformer Architecture
24.5 Pretraining and Transfer Learning
24.6 State of the art
Summary
Bibliographical and Historical Notes
Chapter 25 Computer Vision
25.1 Introduction
25.2 Image Formation
25.3 Simple Image Features
25.4 Classifying Images
25.5 Detecting Objects
25.6 The 3D World
25.7 Using Computer Vision
Summary
Bibliographical and Historical Notes
Chapter 26 Robotics
26.1 Robots
26.2 Robot Hardware
26.3 What kind of problem is robotics solving?
26.4 Robotic Perception
26.5 Planning and Control
26.6 Planning Uncertain Movements
26.7 Reinforcement Learning in Robotics
26.8 Humans and Robots
26.9 Alternative Robotic Frameworks
26.10 Application Domains
Summary
Bibliographical and Historical Notes
Part VII: Conclusions
Chapter 27 Philosophy, Ethics, and Safety of AI
27.1 The Limits of AI
27.2 Can Machines Really Think?
27.3 The Ethics of AI
Summary
Bibliographical and Historical Notes
Chapter 28 The Future of AI
28.1 AI Components
28.2 AI Architectures
Appendix A: Mathematical Background
A.1 Complexity Analysis and O() Notation
A.2 Vectors, Matrices, and Linear Algebra
A.3 Probability Distributions
Bibliographical and Historical Notes
Appendix B: Notes on Languages and Algorithms
B.1 Defining Languages with Backus--Naur Form (BNF)
B.2 Describing Algorithms with Pseudocode
B.3 Online Supplemental Material
Bibliography
Index