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