Artificial intelligence : a modern approach

著者

• Russell, Stuart J. (Stuart Jonathan)
• Norvig, Peter
• Chang, Ming-Wei

書誌事項

Artificial intelligence : a modern approach

Stuart J. Russell and Peter Norvig ; contributing writers: Ming-Wei Chang ... [et al.]

（Pearson series in artificial intelligence / Stuart J. Russell and Peter Norvig, editors）

Pearson, c2022

4th ed., global ed

注記

Includes bibliographical references (p. 1084-1118) and index

内容説明・目次

内容説明

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.

目次

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 SummaryBibliographical and Historical Notes Intelligent Agents Agents and Environments Good Behavior: The Concept of Rationality The Nature of Environments The Structure of Agents SummaryBibliographical 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 SummaryBibliographical 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 SummaryBibliographical 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 SummaryBibliographical 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 SummaryBibliographical 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 SummaryBibliographical 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 SummaryBibliographical and Historical Notes Inference in First-Order Logic Propositional vs. First-Order Inference Unification and First-Order Inference Forward Chaining Backward Chaining Resolution SummaryBibliographical and Historical Notes Knowledge Representation Ontological Engineering Categories and Objects Events Mental Objects and Modal Logic for Categories Reasoning with Default Information SummaryBibliographical 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 SummaryBibliographical 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 SummaryBibliographical 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 SummaryBibliographical and Historical Notes Probabilistic Reasoning over Time Time and Uncertainty Inference in Temporal Models Hidden Markov Models Kalman Filters Dynamic Bayesian Networks SummaryBibliographical 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 SummaryBibliographical and Historical Notes Making Complex Decisions Sequential Decision Problems Algorithms for MDPs Bandit Problems Partially Observable MDPs Algorithms for Solving POMDPs SummaryBibliographical and Historical Notes Multiagent Decision Making Properties of Multiagent Environments Non-Cooperative Game Theory Cooperative Game Theory Making Collective Decisions SummaryBibliographical and Historical Notes Probabilistic Programming Relational Probability Models Open-Universe Probability Models Keeping Track of a Complex World Programs as Probability Models SummaryBibliographical 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 SummaryBibliographical and Historical Notes Knowledge in Learning A Logical Formulation of Learning Knowledge in Learning Exmplanation-Based Leaening Learning Using Relevance Information Inductive Logic Programming SummaryBibliographical and Historical Notes Learning Probabilistic Models Statistical Learning Learning with Complete Data Learning with Hidden Variables: The EM Algorithm SummaryBibliographical 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 SummaryBibliographical 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 SummaryBibliographical 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 SummaryBibliographical 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 SummaryBibliographical 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 SummaryBibliographical and Historical Notes Computer Vision Introduction Image Formation Simple Image Features Classifying Images Detecting Objects The 3D World Using Computer Vision SummaryBibliographical and Historical Notes Chapter VII Conclusions Philosophy, Ethics, and Safety of Al The Limits of Al Can Machines Really Think? The Ethics of Al SummaryBibliographical 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

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詳細情報

• NII書誌ID(NCID): BC07491962
• ISBN:
  • 9781292401133
• 出版国コード: uk
• タイトル言語コード: eng
• 本文言語コード: eng
• 出版地: Harlow
• ページ数/冊数: 1166 p.
• 大きさ: 26 cm
• 分類:
  • DC23 : 006.3
• 件名:
  • LCSH : Artificial intelligence
• 親書誌ID:
  • BB30910242