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Artificial Intelligence: A Modern Approach, Global Edition740
Thelong-anticipated revision of ArtificialIntelligence: A Modern Approach explores the full breadth and depth of the field of artificialintelligence (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 coverageof machine learning, deep learning, transfer learning, multi agent systems,robotics, natural language processing, causality, probabilistic programming,privacy, fairness, and safe AI.
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FROM THE BBC REITH LECTURER 2021 'The most important book I have read in quite some time' Daniel Kahneman; 'A must-read' Max Tegmark; 'The book we've all been waiting for' Sam Harris Humans dream of super-intelligent machines. But what happens if ...
Stuart Russell, Eric H Wefald
The authors argue that a new theoretical foundation for artificial intelligence can be constructed in which rationality is a property of "e;programs"e; within a finite architecture, and their behavior over time in the task environment, rat...
Stuart Russell was born in 1962 in Portsmouth, England. He received his B.A. withfirst-class honours in physics from Oxford University in 1982, and his Ph.D. incomputer science from Stanford in 1986. He then joined the faculty of theUniversity of California at Berkeley, where he is a professor and former chairof computer science, director of the Center for Human-Compatible AI, and holderof the Smith-Zadeh Chair in Engineering. In 1990, he received the PresidentialYoung Investigator Award of the National Science Foundation, and in 1995 he wasco-winner of the Computers and Thought Award. He is a Fellow of the AmericanAssociation for Artificial Intelligence, the Association for ComputingMachinery, and the American Association for the Advancement of Science, and HonoraryFellow of Wadham College, Oxford, and an Andrew Carnegie Fellow. He held theChaire Blaise Pascal in Paris from 2012 to 2014. He has published over 300papers on a wide range of topics in artificial intelligence. His other booksinclude: The Use of Knowledge in Analogy and Induction, Do the Right Thing:Studies in Limited Rationality (with Eric Wefald), and Human Compatible:Artificial Intelligence and the Problem of Control. Peter Norvig iscurrently Director of Research at Google, Inc., and was the directorresponsible for the core Web search algorithms from 2002 to 2005. He is aFellow of the American Association for Artificial Intelligence and theAssociation for Computing Machinery. Previously, he was head of theComputational Sciences Division at NASA Ames Research Center, where he oversawNASA's research and development in artificial intelligence and robotics, andchief scientist at Junglee, where he helped develop one of the first Internetinformation extraction services. He received a B.S. in applied mathematics fromBrown University and a Ph.D. in computer science from the University ofCalifornia at Berkeley. He received the Distinguished Alumni and EngineeringInnovation awards from Berkeley and the Exceptional Achievement Medal fromNASA. He has been a professor at the University of Southern California and aresearch faculty member at Berkeley. His other books are: Paradigms of AIProgramming: Case Studies in Common Lisp, Verbmobil: A Translation System forFace-to-Face Dialog, and Intelligent Help Systems for UNIX. The two authors shared the inaugural AAAI/EAAI Outstanding Educatoraward in 2016.
Part I: Artificial Intelligence 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 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 Part II: Problem Solving 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 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 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 6. Constraint Satisfaction Problems 6.1 Defining ConstraintSatisfaction 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 Part III: Knowledge and Reasoning 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 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 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 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 11. Automated Planning 11.1 Definition of ClassicalPlanning 11.2 Algorithms for Classical Planning 11.3 Heuristics for Planning 11.4 Hierarchical Planning 11.5 Planning and Acting in NondeterministicDomains 11.6 Time, Schedules, and Resources 11.7 Analysis of Planning Approaches 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 Part IV: Uncertain Knowledge and Reasoning 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 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 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 16. Making Simple Decisions 16.1 Combining Beliefs and Desires underUncertainty 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 17. Making Complex