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(eBook) Artificial Intelligence: A Modern Approach, Global Edition 요약정보 및 구매

저자 : Stuart Russell

상품 선택옵션 0 개, 추가옵션 0 개

위시리스트0
판매가격 48,000원
출판사 Pearson
발행일2017
ISBN 9781292153971
언어 KOR
배송비결제 주문시 결제

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  • (eBook) Artificial Intelligence: A Modern Approach, Global Edition
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  • 상품 정보

    상품 상세설명


    one or two-semester, undergraduate or graduate-level courses in Artificial Intelligence.


    The long-anticipated revision of this best-selling text offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence.

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  • 사용후기

    I. Artificial Intelligence


    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 Summary, Bibliographical and Historical Notes, Exercises


    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


    2.5 Summary, Bibliographical and Historical Notes, Exercises


    II. Problem-solving


    3. Solving Problems by Searching


    3.1 Problem-Solving Agents


    3.2 Example Problems


    3.3 Searching for Solutions


    3.4 Uninformed Search Strategies


    3.5 Informed (Heuristic) Search Strategies


    3.6 Heuristic Functions


    3.7 Summary, Bibliographical and Historical Notes, Exercises


    4. Beyond Classical Search


    4.1 Local Search Algorithms and Optimization Problems


    4.2 Local Search in Continuous Spaces


    4.3 Searching with Nondeterministic Actions


    4.4 Searching with Partial Observations


    4.5 Online Search Agents and Unknown Environments


    4.6 Summary, Bibliographical and Historical Notes, Exercises


    5. Adversarial Search


    5.1 Games


    5.2 Optimal Decisions in Games


    5.3 Alpha—Beta Pruning


    5.4 Imperfect Real-Time Decisions


    5.5 Stochastic Games


    5.6 Partially Observable Games


    5.7 State-of-the-Art Game Programs


    5.8 Alternative Approaches


    5.9 Summary, Bibliographical and Historical Notes, Exercises


    6. Constraint Satisfaction Problems


    6.1 Defining Constraint Satisfaction Problems


    6.2 Constraint Propagation: Inference in CSPs


    6.3 Backtracking Search for CSPs


    6.4 Local Search for CSPs


    6.5 The Structure of Problems


    6.6 Summary, Bibliographical and Historical Notes, Exercises


    III. Knowledge, Reasoning, and Planning


    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


    7.8 Summary, Bibliographical and Historical Notes, Exercises


    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


    8.5 Summary, Bibliographical and Historical Notes, Exercises


    9. Inference in First-Order Logic


    9.1 Propositional vs. First-Order Inference


    9.2 Unification and Lifting


    9.3 Forward Chaining


    9.4 Backward Chaining


    9.5 Resolution


    9.6 Summary, Bibliographical and Historical Notes, Exercises


    10. Classical Planning


    10.1 Definition of Classical Planning


    10.2 Algorithms for Planning as State-Space Search


    10.3 Planning Graphs


    10.4 Other Classical Planning Approaches


    10.5 Analysis of Planning Approaches


    10.6 Summary, Bibliographical and Historical Notes, Exercises


    11. Planning and Acting in the Real World


    11.1 Time, Schedules, and Resources


    11.2 Hierarchical Planning


    11.3 Planning and Acting in Nondeterministic Domains


    11.4 Multiagent Planning


    11.5 Summary, Bibliographical and Historical Notes, Exercises


    12 Knowledge Representation


    12.1 Ontological Engineering


    12.2 Categories and Objects


    12.3 Events


    12.4 Mental Events and Mental Objects


    12.5 Reasoning Systems for Categories


    12.6 Reasoning with Default Information


    12.7 The Internet Shopping World


    12.8 Summary, Bibliographical and Historical Notes, Exercises


    IV. Uncertain Knowledge and Reasoning


    13. Quantifying Uncertainty


    13.1 Acting under Uncertainty


    13.2 Basic Probability Notation


    13.3 Inference Using Full Joint Distributions


    13.4 Independence


    13.5 Bayes’ Rule and Its Use


    13.6 The Wumpus World Revisited


    13.7 Summary, Bibliographical and Historical Notes, Exercises


    14. Probabilistic Reasoning


    14.1 Representing Knowledge in an Uncertain Domain


    14.2 The Semantics of Bayesian Networks


    14.3 Efficient Representation of Conditional Distributions


    14.4 Exact Inference in Bayesian Networks


    14.5 Approximate Inference in Bayesian Networks


    14.6 Relational and First-Order Probability Models


    14.7 Other Approaches to Uncertain Reasoning


    14.8 Summary, Bibliographical and Historical Notes, Exercises


    15. Probabilistic Reasoning over Time


    15.1 Time and Uncertainty


    15.2 Inference in Temporal Models


    15.3 Hidden Markov Models


    15.4 Kalman Filters


    15.5 Dynamic Bayesian Networks


    15.6 Keeping Track of Many Objects


    15.7 Summary, Bibliographical and Historical Notes, Exercises


    16. 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 Decision-Theoretic Expert Systems


    16.8 Summary, Bibliographical and Historical Notes, Exercises


    17. Making Complex Decisions


    17.1 Sequential Decision Problems


    17.2 Value Iteration


    17.3 Policy Iteration


    17.4 Partially Observable MDPs


    17.5 Decisions with Multiple Agents: Game Theory


    17.6 Mechanism Design


    17.7 Summary, Bibliographical and Historical Notes, Exercises


    V. Learning


    18. Learning from Examples


    18.1 Forms of Learning


    18.2 Supervised Learning


    18.3 Learning Decision Trees


    18.4 Evaluating and Choosing the Best Hypothesis


    18.5 The Theory of Learning


    18.6 Regression and Classification with Linear Models


    18.7 Artificial Neural Networks


    18.8 Nonparametric Models


    18.9 Support Vector Machines


    18.10 Ensemble Learning


    18.11 Practical Machine Learning


    18.12 Summary, Bibliographical and Historical Notes, Exercises


    19. Knowledge in Learning


    19.1 A Logical Formulation of Learning


    19.2 Knowledge in Learning


    19.3 Explanation-Based Learning


    19.4 Learning Using Relevance Information


    19.5 Inductive Logic Programming


    19.6 Summary, Bibliographical and Historical Notes, Exercises


    20. Learning Probabilistic Models


    20.1 Statistical Learning


    20.2 Learning with Complete Data


    20.3 Learning with Hidden Variables: The EM Algorithm


    20.4 Summary, Bibliographical and Historical Notes, Exercises


    21. Reinforcement Learning


    21.1 Introduction


    21.2 Passive Reinforcement Learning


    21.3 Active Reinforcement Learning


    21.4 Generalization in Reinforcement Learning


    21.5 Policy Search


    21.6 Applications of Reinforcement Learning


    21.7 Summary, Bibliographical and Historical Notes, Exercises


    VI. Communicating, Perceiving, and Acting


    22. Natural Language Processing


    22.1 Language Models


    22.2 Text Classification


    22.3 Information Retrieval


    22.4 Information Extraction


    22.5 Summary, Bibliographical and Historical Notes, Exercises


    23. Natural Language for Communication


    23.1 Phrase Structure Grammars


    23.2 Syntactic Analysis (Parsing)


    23.3 Augmented Grammars and Semantic Interpretation


    23.4 Machine Translation


    23.5 Speech Recognition


    23.6 Summary, Bibliographical and Historical Notes, Exercises


    24. Perception


    24.1 Image Formation


    24.2 Early Image-Processing Operations


    24.3 Object Recognition by Appearance


    24.4 Reconstructing the 3D World


    24.5 Object Recognition from Structural Information


    24.6 Using Vision


    24.7 Summary, Bibliographical and Historical Notes, Exercises


    25. Robotics


    25.1 Introduction


    25.2 Robot Hardware


    25.3 Robotic Perception


    25.4 Planning to Move


    25.5 Planning Uncertain Movements


    25.6 Moving


    25.7 Robotic Software Architectures


    25.8 Application Domains


    25.9 Summary, Bibliographical and Historical Notes, Exercises


    VII. Conclusions


    26 Philosophical Foundations


    26.1 Weak AI: Can Machines Act Intelligently?


    26.2 Strong AI: Can Machines Really Think?


    26.3 The Ethics and Risks of Developing Artificial Intelligence


    26.4 Summary, Bibliographical and Historical Notes, Exercises


    27. AI: The Present and Future


    27.1 Agent Components


    27.2 Agent Architectures


    27.3 Are We Going in the Right Direction?


    27.4 What If AI Does Succeed?


    Appendices


    A. Mathematical Background


    A.1 Complexity Analysis and O() Notation


    A.2 Vectors, Matrices, and Linear Algebra


    A.3 Probability Distributions


    B. Notes on Languages and Algorithms


    B.1 Defining Languages with Backus—Naur Form (BNF)


    B.2 Describing Algorithms with Pseudocode


    B.3 Online Help


    Bibliography


    Index

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