Pearson eText Access Card for Artificial Intelligence: A Modern Approach, Global Edition

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, present concepts in a more unified manner, and offers new or expanded coverage of machine learning, deep learning, transfer learning, multi agent systems, robotics, natural language processing, causality, probabilistic programming, privacy, fairness, and safe AI.

Table of Contents:
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