Artificial Intelligence : A Modern Approach /
Stuart J. Russell and Peter Norvig.
- Fourth edition.
- Noida: Pearson India Education Services Pvt. Ltd., 2026
- 1288 pages cm
- Pearson series in artificial intelligence .
Includes bibliographical references and index.
Chapter I Artificial Intelligence Introduction What Is AI? The Foundations of Artificial Intelligence The History of Artificial Intelligence The State of the Art Risks and Benefits of AI Summary Bibliographical and Historical Notes Intelligent Agents Agents and Environments Good Behavior: The Concept of Rationality The Nature of Environments The Structure of Agents Summary Bibliographical and Historical Notes Chapter II Problem Solving Solving Problems by Searching Problem-Solving Agents Example Problems Search Algorithms Uninformed Search Strategies Informed (Heuristic) Search Strategies Heuristic Functions Summary Bibliographical and Historical Notes Search in Complex Environments Local Search and Optimization Problems Local Search in Continuous Spaces Search with Nondeterministic Actions Search in Partially Observable Environments Online Search Agents and Unknown Environments Summary Bibliographical and Historical Notes Constraint Satisfaction Problems Defining Constraint Satisfaction Problems Constraint Propagation: Inference in CSPs Backtracking Search for CSPs Local Search for CSPs The Structure of Problems Summary Bibliographical and Historical Notes Adversarial Search and Games Game Theory Optimal Decisions in Games Heuristic Alpha--Beta Tree Search Monte Carlo Tree Search Stochastic Games Partially Observable Games Limitations of Game Search Algorithms Summary Bibliographical and Historical Notes Chapter III Knowledge, Reasoning and Planning Logical Agents Knowledge-Based Agents The Wumpus World Logic Propositional Logic: A Very Simple Logic Propositional Theorem Proving Effective Propositional Model Checking Agents Based on Propositional Logic Summary Bibliographical and Historical Notes First-Order Logic Representation Revisited Syntax and Semantics of First-Order Logic Using First-Order Logic Knowledge Engineering in First-Order Logic Summary Bibliographical and Historical Notes Inference in First-Order Logic Propositional vs. First-Order Inference Unification and First-Order Inference Forward Chaining Backward Chaining Resolution Summary Bibliographical and Historical Notes Knowledge Representation Ontological Engineering Categories and Objects Events Mental Objects and Modal Logic for Categories Reasoning with Default Information Summary Bibliographical and Historical Notes Automated Planning Definition of Classical Planning Algorithms for Classical Planning Heuristics for Planning Hierarchical Planning Planning and Acting in Nondeterministic Domains Time, Schedules, and Resources Analysis of Planning Approaches Summary Bibliographical and Historical Notes Chapter IV Uncertain Knowledge and Reasoning Quantifying Uncertainty Acting under Uncertainty Basic Probability Notation Inference Using Full Joint Distributions Independence 12.5 Bayes' Rule and Its Use Naive Bayes Models The Wumpus World Revisited Summary Bibliographical and Historical Notes Probabilistic Reasoning Representing Knowledge in an Uncertain Domain The Semantics of Bayesian Networks Exact Inference in Bayesian Networks Approximate Inference for Bayesian Networks Causal Networks Summary Bibliographical and Historical Notes Probabilistic Reasoning over Time Time and Uncertainty Inference in Temporal Models Hidden Markov Models Kalman Filters Dynamic Bayesian Networks Summary Bibliographical and Historical Notes Making Simple Decisions Combining Beliefs and Desires under Uncertainty The Basis of Utility Theory Utility Functions Multiattribute Utility Functions Decision Networks The Value of Information Unknown Preferences Summary Bibliographical and Historical Notes Making Complex Decisions Sequential Decision Problems Algorithms for MDPs Bandit Problems Partially Observable MDPs Algorithms for Solving POMDPs Summary Bibliographical and Historical Notes Multiagent Decision Making Properties of Multiagent Environments Non-Cooperative Game Theory Cooperative Game Theory Making Collective Decisions Summary Bibliographical and Historical Notes Probabilistic Programming Relational Probability Models Open-Universe Probability Models Keeping Track of a Complex World Programs as Probability Models Summary Bibliographical and Historical Notes Chapter V Machine Learning Learning from Examples Forms of Leaming Supervised Learning . Learning Decision Trees . Model Selection and Optimization The Theory of Learning Linear Regression and Classification Nonparametric Models Ensemble Learning Developing Machine Learning Systen Summary Bibliographical and Historical Notes Knowledge in Learning A Logical Formulation of Learning Knowledge in Learning Exmplanation-Based Leaening Learning Using Relevance Information Inductive Logic Programming Summary Bibliographical and Historical Notes Learning Probabilistic Models Statistical Learning Learning with Complete Data Learning with Hidden Variables: The EM Algorithm Summary Bibliographical and Historical Notes Deep Learning Simple Feedforward Networks Computation Graphs for Deep Learning Convolutional Networks Learning Algorithms Generalization Recurrent Neural Networks Unsupervised Learning and Transfer Learning Applications Summary Bibliographical and Historical Notes Reinforcement Learning Learning from Rewards Passive Reinforcement Learning Active Reinforcement Learning Generalization in Reinforcement Learning Policy Search Apprenticeship and Inverse Reinforcement Leaming Applications of Reinforcement Learning Summary Bibliographical and Historical Notes Chapter VI Communicating, perceiving, and acting Natural Language Processing Language Models Grammar Parsing Augmented Grammars Complications of Real Natural Languagr Natural Language Tasks Summary Bibliographical and Historical Notes Deep Learning for Natural Language Processing Word Embeddings Recurrent Neural Networks for NLP Sequence-to-Sequence Models The Transformer Architecture Pretraining and Transfer Learning State of the art Summary Bibliographical and Historical Notes Robotics Robots Robot Hardware What kind of problem is robotics solving? Robotic Perception Planning and Control Planning Uncertain Movements Reinforcement Laming in Robotics Humans and Robots Alternative Robotic Frameworks Application Domains Summary Bibliographical and Historical Notes Computer Vision Introduction Image Formation Simple Image Features Classifying Images Detecting Objects The 3D World Using Computer Vision Summary Bibliographical and Historical Notes Chapter VII Conclusions Philosophy, Ethics, and Safety of Al The Limits of Al Can Machines Really Think? The Ethics of Al Summary Bibliographical and Historical Notes The Future of AI Al Components Al Architectures A Mathematical Background A.1 Complexity Analysis and O0 Notation A.2 Vectors, Matrices, and Linear Algebra A.3 Probability Distributions Bibliographical and Historical Notes
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
Bibliography Index
Artificial Intelligence: A Modern Approach, Global Edition, 4th Edition (AI), explores the full breadth and depth of the field, delving into the advanced methods of reasoning, deep learning, perception, and mathematics.
From robotic planetary explorers to online services with billions of users, the textbook studies a wide range of current methods and technologies, further considering the ethical values of practicing the discipline.
With a new site containing all the exercises, this leading text is a must-read edition, offering a multi-faceted approach to this expanding subject.