Artificial Intelligence: A Modern Approach, Global Edition

<div class="c-non-traditional-number-list_container"> <h3><strong>Chapter I&nbsp; Artificial Intelligence</strong></h3> <ol> <li><strong>Introduction</strong> <ul> <li>What Is AI?</li> <li>The Foundations of Artificial Intelligence</li> <li>The History of Artificial Intelligence</li> <li>The State of the Art</li> <li>Risks and Benefits of AI</li> </ul><h4 class="h5">Summary</h4><h4 class="h5">Bibliographical and Historical Notes</h4></li> <li><strong>Intelligent Agents</strong> <ul> <li>Agents and Environments</li> <li>Good Behavior: The Concept of Rationality</li> <li>The Nature of Environments</li> <li>The Structure of Agents</li> </ul><h4 class="h5">Summary</h4><h4 class="h5">Bibliographical and Historical Notes</h4></li> <h3><strong>Chapter II&nbsp; Problem Solving</strong></h3> <li><strong>Solving Problems by Searching</strong> <ul> <li>Problem-Solving Agents</li> <li>Example Problems</li> <li>Search Algorithms</li> <li>Uninformed Search Strategies</li> <li>Informed (Heuristic) Search Strategies</li> <li>Heuristic Functions</li> </ul><h4 class="h5">Summary</h4><h4 class="h5">Bibliographical and Historical Notes</h4></li> <li><strong>Search in Complex Environments</strong> <ul> <li>Local Search and Optimization Problems</li> <li>Local Search in Continuous Spaces</li> <li>Search with Nondeterministic Actions</li> <li>Search in Partially Observable Environments</li> <li>Online Search Agents and Unknown Environments</li> </ul><h4 class="h5">Summary</h4><h4 class="h5">Bibliographical and Historical Notes</h4></li> <li><strong>Constraint Satisfaction Problems</strong> <ul> <li>Defining Constraint Satisfaction Problems</li> <li>Constraint Propagation: Inference in CSPs</li> <li>Backtracking Search for CSPs</li> <li>Local Search for CSPs</li> <li>The Structure of Problems</li> </ul><h4 class="h5">Summary</h4><h4 class="h5">Bibliographical and Historical Notes</h4></li> <li><strong>Adversarial Search and Games</strong> <ul> <li>Game Theory</li> <li>Optimal Decisions in Games</li> <li>Heuristic Alpha--Beta Tree Search</li> <li>Monte Carlo Tree Search</li> <li>Stochastic Games</li> <li>Partially Observable Games</li> <li>Limitations of Game Search Algorithms</li> </ul><h4 class="h5">Summary</h4><h4 class="h5">Bibliographical and Historical Notes</h4></li> <h3><strong>Chapter III&nbsp; Knowledge, Reasoning and Planning</strong></h3> <li><strong>Logical Agents</strong> <ul> <li>Knowledge-Based Agents</li> <li>The Wumpus World</li> <li>Logic</li> <li>Propositional Logic: A Very Simple Logic</li> <li>Propositional Theorem Proving</li> <li>Effective Propositional Model Checking</li> <li>Agents Based on Propositional Logic</li> </ul><h4 class="h5">Summary</h4><h4 class="h5">Bibliographical and Historical Notes</h4></li> <li><strong>First-Order Logic</strong> <ul> <li>Representation Revisited</li> <li>Syntax and Semantics of First-Order Logic</li> <li>Using First-Order Logic</li> <li>Knowledge Engineering in First-Order Logic</li> </ul><h4 class="h5">Summary</h4><h4 class="h5">Bibliographical and Historical Notes</h4></li> <li><strong>Inference in First-Order Logic</strong> <ul> <li>Propositional vs. First-Order Inference</li> <li>Unification and First-Order Inference</li> <li>Forward Chaining</li> <li>Backward Chaining</li> <li>Resolution</li> </ul><h4 class="h5">Summary</h4><h4 class="h5">Bibliographical and Historical Notes</h4></li> <li><strong>Knowledge Representation</strong> <ul> <li>Ontological Engineering</li> <li>Categories and Objects</li> <li>Events</li> <li>Mental Objects and Modal Logic</li> <li>for Categories</li> <li>Reasoning with Default Information</li> </ul><h4 class="h5">Summary</h4><h4 class="h5">Bibliographical and Historical Notes</h4></li> <li><strong>Automated Planning</strong> <ul> <li>Definition of Classical Planning</li> <li>Algorithms for Classical Planning</li> <li>Heuristics for Planning</li> <li>Hierarchical Planning</li> <li>Planning and Acting in Nondeterministic Domains</li> <li>Time, Schedules, and Resources</li> <li>Analysis of Planning Approaches</li> </ul><h4 class="h5">Summary</h4><h4 class="h5">Bibliographical and Historical Notes</h4></li> <h3><strong>Chapter IV&nbsp; Uncertain Knowledge and Reasoning</strong></h3> <li><strong>Quantifying Uncertainty</strong> <ul> <li>Acting under Uncertainty</li> <li>Basic Probability Notation</li> <li>Inference Using Full Joint Distributions</li> <li>Independence 12.5 Bayes' Rule and Its Use</li> <li>Naive Bayes Models</li> <li>The Wumpus World Revisited</li> </ul><h4 class="h5">Summary</h4><h4 class="h5">Bibliographical and Historical Notes</h4></li> <li><strong>Probabilistic Reasoning</strong> <ul> <li>Representing Knowledge in an Uncertain Domain</li> <li>The Semantics of Bayesian Networks</li> <li>Exact Inference in Bayesian Networks</li> <li>Approximate Inference for Bayesian Networks</li> <li>Causal Networks</li> </ul><h4 class="h5">Summary</h4><h4 class="h5">Bibliographical and Historical Notes</h4></li> <li><strong>Probabilistic Reasoning over Time</strong> <ul> <li>Time and Uncertainty</li> <li>Inference in Temporal Models</li> <li>Hidden Markov Models</li> <li>Kalman Filters</li> <li>Dynamic Bayesian Networks</li> </ul><h4 class="h5">Summary</h4><h4 class="h5">Bibliographical and Historical Notes</h4></li> <li><strong>Making Simple Decisions</strong> <ul> <li>Combining Beliefs and Desires under Uncertainty</li> <li>The Basis of Utility Theory</li> <li>Utility Functions</li> <li>Multiattribute Utility Functions</li> <li>Decision Networks</li> <li>The Value of Information</li> <li>Unknown Preferences</li> </ul><h4 class="h5">Summary</h4><h4 class="h5">Bibliographical and Historical Notes</h4></li> <li><strong>Making Complex Decisions</strong> <ul> <li>Sequential Decision Problems</li> <li>Algorithms for MDPs</li> <li>Bandit Problems</li> <li>Partially Observable MDPs</li> <li>Algorithms for Solving POMDPs</li> </ul><h4 class="h5">Summary</h4><h4 class="h5">Bibliographical and Historical Notes</h4></li> <li><strong>Multiagent Decision Making</strong> <ul> <li>Properties of Multiagent Environments</li> <li>Non-Cooperative Game Theory</li> <li>Cooperative Game Theory</li> <li>Making Collective Decisions</li> </ul><h4 class="h5">Summary</h4><h4 class="h5">Bibliographical and Historical Notes</h4></li> <li><strong>Probabilistic Programming</strong> <ul> <li>Relational Probability Models</li> <li>Open-Universe Probability Models</li> <li>Keeping Track of a Complex World</li> <li>Programs as Probability Models</li> </ul><h4 class="h5">Summary</h4><h4 class="h5">Bibliographical and Historical Notes</h4></li> <h3><strong>Chapter V&nbsp; Machine Learning</strong></h3> <li><strong>Learning from Examples</strong> <ul> <li>Forms of Leaming</li> <li>Supervised Learning .</li> <li>Learning Decision Trees .</li> <li>Model Selection and Optimization</li> <li>The Theory of Learning</li> <li>Linear Regression and Classification</li> <li>Nonparametric Models</li> <li>Ensemble Learning</li> <li>Developing Machine Learning Systen</li> </ul><h4 class="h5">Summary</h4><h4 class="h5">Bibliographical and Historical Notes</h4></li> <li><strong>Knowledge in Learning</strong> <ul> <li>A Logical Formulation of Learning</li> <li>Knowledge in Learning</li> <li>Exmplanation-Based Leaening</li> <li>Learning Using Relevance Information</li> <li>Inductive Logic Programming</li> </ul><h4 class="h5">Summary</h4><h4 class="h5">Bibliographical and Historical Notes</h4></li> <li><strong>Learning Probabilistic Models</strong> <ul> <li>Statistical Learning</li> <li>Learning with Complete Data</li> <li>Learning with Hidden Variables: The EM Algorithm</li> </ul><h4 class="h5">Summary</h4><h4 class="h5">Bibliographical and Historical Notes</h4></li> <li><strong>Deep Learning</strong> <ul> <li>Simple Feedforward Networks</li> <li>Computation Graphs for Deep Learning</li> <li>Convolutional Networks</li> <li>Learning Algorithms</li> <li>Generalization</li> <li>Recurrent Neural Networks</li> <li>Unsupervised Learning and Transfer Learning</li> <li>Applications</li> </ul><h4 class="h5">Summary</h4><h4 class="h5">Bibliographical and Historical Notes</h4></li> <li><strong>Reinforcement Learning</strong> <ul> <li>Learning from Rewards</li> <li>Passive Reinforcement Learning</li> <li>Active Reinforcement Learning</li> <li>Generalization in Reinforcement Learning</li> <li>Policy Search</li> <li>Apprenticeship and Inverse Reinforcement Leaming</li> <li>Applications of Reinforcement Learning</li> </ul><h4 class="h5">Summary</h4><h4 class="h5">Bibliographical and Historical Notes</h4></li> <h3><strong>Chapter VI&nbsp; Communicating, perceiving, and acting</strong></h3> <li><strong>Natural Language Processing</strong> <ul> <li>Language Models</li> <li>Grammar</li> <li>Parsing</li> <li>Augmented Grammars</li> <li>Complications of Real Natural Languagr</li> <li>Natural Language Tasks</li> </ul><h4 class="h5">Summary</h4><h4 class="h5">Bibliographical and Historical Notes</h4></li> <li><strong>Deep Learning for Natural Language Processing</strong> <ul> <li>Word Embeddings</li> <li>Recurrent Neural Networks for NLP</li> <li>Sequence-to-Sequence Models</li> <li>The Transformer Architecture</li> <li>Pretraining and Transfer Learning</li> <li>State of the art</li> </ul><h4 class="h5">Summary</h4><h4 class="h5">Bibliographical and Historical Notes</h4></li> <li><strong>Robotics</strong> <ul> <li>Robots</li> <li>Robot Hardware</li> <li>What kind of problem is robotics solving?</li> <li>Robotic Perception</li> <li>Planning and Control</li> <li>Planning Uncertain Movements</li> <li>Reinforcement Laming in Robotics</li> <li>Humans and Robots</li> <li>Alternative Robotic Frameworks</li> <li>Application Domains</li> </ul><h4 class="h5">Summary</h4><h4 class="h5">Bibliographical and Historical Notes</h4></li> <li><strong>Computer Vision</strong> <ul> <li>Introduction</li> <li>Image Formation</li> <li>Simple Image Features</li> <li>Classifying Images</li> <li>Detecting Objects</li> <li>The 3D World</li> <li>Using Computer Vision</li> </ul><h4 class="h5">Summary</h4><h4 class="h5">Bibliographical and Historical Notes</h4></li> <h3><strong>Chapter VII&nbsp; Conclusions</strong></h3> <li><strong>Philosophy, Ethics, and Safety of Al</strong> <ul> <li>The Limits of Al</li> <li>Can Machines Really Think?</li> <li>The Ethics of Al</li> </ul><h4 class="h5">Summary</h4><h4 class="h5">Bibliographical and Historical Notes</h4></li> <li><strong>The Future of AI</strong> <ul> <li>Al Components</li> <li>Al Architectures</li> </ul></li> </ol> </div> <div class="c-un-numbered-list_container"> <h6><strong>A Mathematical Background</strong></h6> <ul> <li>A.1 Complexity Analysis and O0 Notation</li> <li>A.2 Vectors, Matrices, and Linear Algebra</li> <li>A.3 Probability Distributions</li> <li>Bibliographical and Historical Notes</li> </ul> <p>&nbsp;</p> <h6><strong>B Notes on Languages and Algorithms</strong></h6> <ul> <li>B.1 Defining Languages with Backus-Naur Form (BNF)</li> <li>B.2 Describing Algorithms with Pseudocode</li> <li>B.3 Online Supplemental Material</li> </ul> <p>&nbsp;</p> <h5 class="h6"><strong>Bibliography</strong></h5> <h5 class="h6"><strong>Index</strong></h5> </div>