Artificial Intelligence:
Introduction:
What is AI?
Foundations of artificial intelligence
History of artificial intelligence
State of the art
Summary, bibliographical and historical notes, exercises
Intelligent agents:
Agents and environments
Good behavior: concept of rationality
Nature of environments
Structure of agents
Problem-Solving:
Solving problems by searching:
Problem-solving agents
Example problems
Searching for solutions
Uniformed search strategies
Informed (heuristic) search strategies
Heuristic functions
Beyond classical search:
Local search algorithms and optimization problems
Local search in continuous spaces
Searching with nondeterministic actions
Searching with partial observations
Online search agents and unknown environments
Adversarial search:
Games
Optimal decisions in games
Alpha-beta pruning
Imperfect real-time decisions
Stochastic games
Partially observable games
State-of-the-art game programs
Alternative approaches
Constraint satisfaction problems:
Defining constraint satisfaction problems
Constraint propagation: inference in CSPs
Backtracking search for CSPs
Local search for CSPs
Structure of problems
Knowledge, Reasoning, And Planning:
Logical agents:
Knowledge-based agents
Wumpus world
Logic
Propositional logic: a very simple logic
Propositional theorem proving
Effective propositional model checking
Agents based on propositional logic
First-order logic:
Representation revisited
Syntax and semantics of first-order logic
Using first-order logic
Knowledge engineering in first-order logic
Decisions with multiple agents: game theory
Mechanism design
Learning:
Learning from examples:
Forms of learning
Supervised learning
Learning decision trees
Evaluating and choosing the best hypothesis
Theory of learning
Regression and classification with linear models
Artificial neural networks
Nonparametric models
Support vector machines
Ensemble learning
Practical machine learning
Knowledge in learning:
Logical formulation of learning
Knowledge in learning
Explanation-based learning
Learning using relevance information
Inductive logic programming
Learning probabilistic models:
Statistical learning
Learning with complete data
Learning with hidden variables: the EM algorithm
Reinforcement learning:
Introduction
Passive reinforcement learning
Active reinforcement learning
Generalization in reinforcement learning
Policy search
Applications of reinforcement learning
Communicating, Perceiving, And Acting:
Natural language processing:
Language models
Text classification
Information retrieval
Information extraction
Natural language for communication:
Phrase structure grammars
Syntactic analysis (parsing)
Augmented grammars and semantic interpretation
Machine translation
Speech recognition
Perception:
Image formation
Early image-processing operations
Object recognition by appearance
Reconstructing the 3D world
Object recognition for structural information
Using vision
Robotics:
Robot hardware
Robotic perception
Planning to move
Planning uncertain movements
Moving
Robotic software architectures
Application domains
Conclusions:
Philosophical foundations:
Weak AI: can machines act intelligently?
Strong AI: can machines really think?
Ethics and risks of developing artificial intelligence
AI: the present and future:
Agent components
Agent architectures
Are we going in the right direction?
What if AI does succeed?
Mathematical background:
Complexity analysis and O() notation
Vectors, matrices, and linear algebra
Probability distribution
Notes on languages and algorithms:
Defining languages with Backus-Naur Form (BNF)
Describing algorithms with pseudocode
Online help
Bibliography
Index.

In this third edition, the authors have updated the treatment of all major areas. A new organizing principle--the representational dimension of atomic, factored, and structured models--has been added. Significant new material has been provided in areas such as partially observable search, contingency planning, hierarchical planning, relational and first-order probability models, regularization and loss functions in machine learning, kernel methods, Web search engines, information extraction, and learning in vision and robotics. The book also includes hundreds of new exercises.

CSCI323