Part I: ArtificialIntelligence 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. Constraint Satisfaction Problems     5.1  Defining Constraint Satisfaction Problems     5.2  Constraint Propagation: Inference in CSPs     5.3  Backtracking Search for CSPs     5.4  Local Search for CSPs     5.5  The Structure of Problems 6. Adversarial Search and Games     6.1  Game Theory     6.2  Optimal Decisions in Games     6.3  Heuristic Alpha--Beta Tree Search     6.4  Monte Carlo Tree Search     6.5  Stochastic Games     6.6  Partially Observable Games     6.7  Limitations of Game Search Algorithms   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 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 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. 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 16. 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   Part V: Learning 17. Multiagent Decision Making     17.1  Properties of Multiagent Environments     17.2  Non-Cooperative Game Theory     17.3  Cooperative Game Theory     17.4  Making Collective Decisions 18. ProbabilisticProgramming     18.1  Relational Probability Models     18.2  Open-Universe Probability Models     18.3  Keeping Track of a Complex World     18.4  Programs as Probability Models 19. Learning fromExamples     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   20. Knowledge inLearning    20.1 A Logical Formulation of Learning    20.2 Knowledge in Learning    20.3 Explanation-Based Learning    20.4 Learning Using Relevance Information    20.5 Inductive Logic Programming   21. LearningProbabilistic Models     21.1  Statistical Learning     21.2  Learning with Complete Data     21.3  Learning with Hidden Variables: The EM Algorithm 22. Deep Learning     22.1  Simple Feedforward Networks     22.2  Mixing and matching models, loss functions andoptimizers     22.3  Loss functions     22.4  Models     22.5  Optimization Algorithms     22.6  Generalization     22.7  Recurrent neural networks     22.8  Unsupervised, semi-supervised and transferlearning     22.9  Applications   Part VI: Communicating, Perceiving, and Acting 23. Reinforcement Learning     23.1  Learning from Rewards     23.2  Passive Reinforcement Learning     23.3  Active Reinforcement Learning     23.4  Safe Exploration     23.5  Generalization in Reinforcement Learning     23.6  Policy Search     23.7  Applications of Reinforcement Learning 24. Natural Language Processing     24.1  Language Models     24.2  Grammar     24.3  Parsing     24.4  Augmented Grammars     24.5  Complications of Real Natural Language     24.6  Natural Language Tasks 25. Deep Learning for Natural Language Processing     25.1  Limitations of Feature-Based NLP Models     25.2  Word Embeddings     25.3  Recurrent Neural Networks     25.4  Sequence-to-sequence Models     25.5  The Transformer Architecture     25.6  Pretraining and Transfer Learning 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 27. Computer Vision     27.1 Introduction     27.2 Image Formation     27.3  Simple Image Features     27.4 Classifying Images     27.5 Detecting Objects     27.6 The 3D World     27.7 Using Computer Vision   Part VII: Conclusions 28. Philosophy and Ethics of AI     28.1  Weak AI: What are the Limits of AI?     28.2  Strong AI: Can Machines Really Think?     28.3  The Ethics of AI 29. The Future of AI     29.1  AI Components     29.2  AI Architectures   Appendix A:Mathematical Background     A.1  Complexity Analysis and O() Notation     A.2  Vectors, Matrices, and Linear Algebra     A.3  Probability Distributions 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

Offer the mostcomprehensive, up-to-date introduction to the theory and practice of artificialintelligence ·        Nontechnical learning material introducesmajor concepts using intuitive explanations, before going into mathematical oralgorithmic details. The nontechnical language makes the book accessible to abroader range of readers. ·        A unified approach to AI showsstudents how the various subfields of AI fit together to build actual, usefulprograms. ·        UPDATED - The basic definition of AI systems isgeneralized to eliminate the standard assumption that the objective is fixedand known by the intelligent agent; instead, the agent may be uncertain aboutthe true objectives of the human(s) on whose behalf it operates. ·        In-depth coverage of both basic and advancedtopics provides students with a basic understanding of the frontiers ofAI without compromising complexity and depth. ·        The Author-Maintained Website at http://aima.cs.berkeley.edu/ includestext-related comments and discussions, exercises, an online code repository,Instructor Resources, and more! ·        UPDATED - Interactive student exercises are nowfeatured on the website to allow for continuous updating and additions. ·        UPDATED - Online software givesstudents more opportunities to complete projects, including implementations ofthe algorithms in the book, plus supplemental coding examples and applicationsin Python, Java, and Javascript. ·        A flexible format makesthe text adaptable for varying instructors' preferences. ·        Stay current with the latest technologies andpresent concepts in a more unified manner ·        NEW - New chapters featureexpanded coverage of probabilistic programming (Ch. 18); multiagentdecision making (Ch. 17 with Michael Wooldridge); deeplearning (Ch. 22 with Ian Goodfellow); and deep learning fornatural language processing (Ch. 25 with Jacob Devlin and Mei-WingChang). ·        UPDATED - Increased coverageof machine learning. ·        UPDATED - Significantlyupdated material on robotics includes robots that interactwith humans and the application of reinforcement learning to robotics. ·        NEW - New section on causality byJudea Pearl. ·        NEW - New sections on MonteCarlo search for games and robotics. ·        NEW - New sections on transferlearning for deep learning in general and for natural language. ·        NEW - New sections on privacy,fairness, the future of work, and safe AI. ·        NEW - Extensive coverageof recent advances in AI applications. ·        UPDATED - Revisedcoverage of computer vision, natural language understanding, and speechrecognition reflect the impact of deep learning methods on thesefields.

Offer the mostcomprehensive, up-to-date introduction to the theory and practice of artificialintelligence ·        The basic definition of AI systems is generalized to eliminate the standardassumption that the objective is fixed and known by the intelligent agent;instead, the agent may be uncertain about the true objectives of the human(s)on whose behalf it operates. ·        The Author-Maintained Website at http://aima.cs.berkeley.edu/ includestext-related comments and discussions, exercises, an online code repository,Instructor Resources, and more! ·        Interactive student exercises are now featured onthe website to allow for continuous updating and additions. ·        Updated online software gives students moreopportunities to complete projects, including implementations of the algorithmsin the book, plus supplemental coding examples and applications in Python,Java, and Javascript. Stay current with thelatest technologies and present concepts in a more unified manner ·         NEW - New chapters feature expanded coverageof probabilistic programming (Ch. 18); multiagentdecision making (Ch. 17 with Michael Wooldridge); deep learning (Ch.22 with Ian Goodfellow); and deep learning for natural languageprocessing (Ch. 25 with Jacob Devlin and Mei-Wing Chang). Increased coverage of machine learning. Significantly updated material on robotics includes robots that interact with humans and the application of reinforcement learning to robotics. New section on causality by Judea Pearl. New sections on Monte Carlo search for games and robotics. New sections on transfer learning for deep learning in general and for natural language. New sections on privacy, fairness, the future of work, and safe AI. Extensive coverage of recent advances in AI applications. Revised coverage of computer vision, natural language understanding, and speech recognition reflect the impact of deep learning methods on these fields.