Artificial Intelligence A Modern Approach 4th edition by Stuart Russell, Peter Norvig 9780134671932 0134671937

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ISBN 10: 0134671937
ISBN 13: 9780134671932 
Author: Stuart Russell; Peter Norvig

The most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence The long-anticipated revision of Artificial Intelligence: A Modern Approach explores the full breadth and depth of the field of artificial intelligence (AI). The 4th Edition brings readers up to date on the latest technologies, presents concepts in a more unified manner, and offers new or expanded coverage of machine learning, deep learning, transfer learning, multiagent systems, robotics, natural language processing, causality, probabilistic programming, privacy, fairness, and safe AI.

Artificial Intelligence A Modern Approach 4th Table of contents:

Chapter 1: Introduction 1.1 What Is AI? 1.1.1 Acting humanly: The Turing test approach
1.1.2 Thinking humanly: The cognitive modeling approach
1.1.3 Thinking rationally: The “laws of thought” approach
1.1.4 Acting rationally: The rational agent approach
1.1.5 Beneficial machines
1.2 The Foundations of Artificial Intelligence 1.2.1 Philosophy
1.2.2 Mathematics
1.2.3 Economics
1.2.4 Neuroscience
1.2.5 Psychology
1.2.6 Computer engineering
1.2.7 Control theory and cybernetics
1.2.8 Linguistics
1.3 The History of Artificial Intelligence 1.3.1 The inception of artificial intelligence (1943–1956)
1.3.2 Early enthusiasm, great expectations (1952–1969)
1.3.3 A dose of reality (1966–1973)
1.3.4 Expert systems (1969–1986)
1.3.5 The return of neural networks (1986–present)
1.3.6 Probabilistic reasoning and machine learning (1987–present)
1.3.7 Big data (2001–present)
1.3.8 Deep learning (2011–present)
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
2.2.1 Performance measures
2.2.2 Rationality
2.2.3 Omniscience, learning, and autonomy
2.3 The Nature of Environments
2.3.1 Specifying the task environment
2.3.2 Properties of task environments
2.4 The Structure of Agents
2.4.1 Agent programs
2.4.2 Simple reflex agents
2.4.3 Model-based reflex agents
2.4.4 Goal-based agents
2.4.5 Utility-based agents
2.4.6 Learning agents
2.4.7 How the components of agent programs work
Summary
Bibliographical and Historical Notes

Chapter 3: Solving Problems by Searching 3.1 Problem-Solving Agents
3.1.1 Search problems and solutions
3.1.2 Formulating problems
3.2 Example Problems
3.2.1 Standardized problems
3.2.2 Real-world problems
3.3 Search Algorithms
3.3.1 Best-first search
3.3.2 Search data structures
3.3.3 Redundant paths
3.3.4 Measuring problem-solving performance
3.4 Uninformed Search Strategies
3.4.1 Breadth-first search
3.4.2 Dijkstra’s algorithm or uniform-cost search
3.4.3 Depth-first search and the problem of memory
3.4.4 Depth-limited and iterative deepening search
3.4.5 Bidirectional search
3.4.6 Comparing uninformed search algorithms
3.5 Informed (Heuristic) Search Strategies
3.5.1 Greedy best-first search
3.5.2 A* search
3.5.3 Search contours
3.5.4 Satisficing search: Inadmissible heuristics and weighted A*
3.5.5 Memory-bounded search
3.5.6 Bidirectional heuristic search
3.6 Heuristic Functions
3.6.1 The effect of heuristic accuracy on performance
3.6.2 Generating heuristics from relaxed problems
3.6.3 Generating heuristics from subproblems: Pattern databases
3.6.4 Generating heuristics with landmarks
3.6.5 Learning to search better
3.6.6 Learning heuristics from experience
Summary
Bibliographical and Historical Notes

Chapter 4: Search in Complex Environments 4.1 Local Search and Optimization Problems
4.1.1 Hill-climbing search
4.1.2 Simulated annealing
4.1.3 Local beam search
4.1.4 Evolutionary algorithms
4.2 Local Search in Continuous Spaces
4.3 Search with Nondeterministic Actions
4.3.1 The erratic vacuum world
4.3.2 And–or search trees
4.3.3 Try, try again
4.4 Search in Partially Observable Environments
4.4.1 Searching with no observation
4.4.2 Searching in partially observable environments
4.4.3 Solving partially observable problems
4.4.4 An agent for partially observable environments
4.5 Online Search Agents and Unknown Environments
4.5.1 Online search problems
4.5.2 Online search agents
4.5.3 Online local search
4.5.4 Learning in online search
Summary
Bibliographical and Historical Notes

Chapter 5: Adversarial Search and Games 5.1 Game Theory
5.1.1 Two-player zero-sum games
5.2 Optimal Decisions in Games
5.2.1 The minimax search algorithm
5.2.2 Optimal decisions in multiplayer games
5.2.3 Alpha–Beta Pruning
5.2.4 Move ordering
5.3 Heuristic Alpha–Beta Tree Search
5.3.1 Evaluation functions
5.3.2 Cutting off search
5.3.3 Forward pruning
5.3.4 Search versus lookup
5.4 Monte Carlo Tree Search
5.5 Stochastic Games
5.5.1 Evaluation functions for games of chance
5.6 Partially Observable Games
5.6.1 Kriegspiel: Partially observable chess
5.6.2 Card games
5.7 Limitations of Game Search Algorithms
Summary
Bibliographical and Historical Notes

Chapter 6: Constraint Satisfaction Problems 6.1 Defining Constraint Satisfaction Problems
6.1.1 Example problem: Map coloring
6.1.2 Example problem: Job-shop scheduling
6.1.3 Variations on the CSP formalism
6.2 Constraint Propagation: Inference in CSPs
6.2.1 Node consistency
6.2.2 Arc consistency
6.2.3 Path consistency
6.2.4 K-consistency
6.2.5 Global constraints
6.2.6 Sudoku
6.3 Backtracking Search for CSPs
6.3.1 Variable and value ordering
6.3.2 Interleaving search and inference
6.3.3 Intelligent backtracking: Looking backward
6.3.4 Constraint learning
6.4 Local Search for CSPs
6.5 The Structure of Problems
6.5.1 Cutset conditioning
6.5.2 Tree decomposition
6.5.3 Value symmetry
Summary

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