عدم قطعیت در هوش مصنوعی ۲۰۱۴
Uncertainty in Artificial Intelligence 2014

1. Front Cover

2. Uncertainty in Artificial Intelligence

3. Copyright Page

4. Table of Contents

5. Preface

6. Acknowledgments

7. Chapter 1. Ending-based Strategies for Part-of-speech Tagging

8. Chapter 2. An evaluation of an algorithm for inductive learning of Bayesian belief networks using simulated data sets

9. Chapter 3. Probabilistic Constraint Satisfaction with Non-Gaussian Noise

10. Chapter 4. A Bayesian Method Reexamined

11. Chapter 5. Laplace's Method Approximations for Probabilistic Inference in Belief Networks with Continuous Variables

12. Chapter 6. Generating New Beliefs From Old

13. Chapter 7. Counterfactual Probabilities: Computational Methods, Bounds and Applications

14. Chapter 8. Modus Ponens Generating Function in the Class of Λ-valuations of Plausibility

15. Chapter 9. Approximation Algorithms for the Loop Cutset Problem

16. Chapter 10. Possibility and necessity functions over non-classical logics

17. Chapter 11. Exploratory Model Building

18. Chapter 12. Learning in Multi-Level Stochastic Games with Delayed Information

19. Chapter 13. Planning with External Events

20. Chapter 14. Properties of Bayesian Belief Network Learning Algorithms

21. Chapter 15. A Stratified Simulation Scheme for Inference in Bayesian Belief Networks

22. Chapter 16. Proposal: Interactive Media for Research in Uncertainty

23. Chapter 17. Efficient Estimation of the Value of Information in Monte Carlo Models

24. Chapter 18. Symbolic Probabilistic Inference in large BN20 networks

25. Chapter 19. Action Networks: A Framework for Reasoning about Actions and Change under Uncertainty

26. Chapter 20. On the Relation between Kappa Calculus and Probabilistic Reasoning

27. Chapter 21. A Structured, Probabilistic Representation of Action

28. Chapter 22. Integrating Planning and Execution in Stochastic Domains

29. Chapter 23. Localized Partial Evaluation of Belief Networks

30. Chapter 24. A Probabilistic Model of Action for Least-Commitment Planning with Information Gathering

31. Chapter 25. Some Properties of Joint Probability Distributions

32. Chapter 26. An ordinal view of independence with application to plausible reasoning

33. Chapter 27. Penalty logic and its link with Dempster-Shafer theory

34. Chapter 28. Value of Evidence on Influence Diagrams

35. Chapter 29. Conditional independence in possibility theory

36. Chapter 30. Backward Simulation in Bayesian Networks

37. Chapter 31. Learning Gaussian Networks

38. Chapter 32. On testing whether an Embedded Bayesian Network represents a probability model

39. Chapter 33. Epsilon-Safe Planning

40. Chapter 34. Generating Bayesian Networks from Probability Logic Knowledge Bases

41. Chapter 35. Abstracting Probabilistic Actions

42. Chapter 36. On Modal Logics for Qualitative Possibility in a Fuzzy Setting

43. Chapter 37. A New Look at Causal Independence

44. Chapter 38. Learning Bayesian Networks: The Combination of Knowledge and Statistical Data

45. Chapter 39. A Decision-Based View of Causality

46. Chapter 40. Probabilistic Description Logics

47. Chapter 41. An Experimental Comparison of Numerical and Qualitative Probabilistic Reasoning

48. Chapter 42. An Alternative Proof Method for Possibilistic Logic and its Application to Terminological Logics

49. Chapter 43. Possibilistic Conditioning and Propagation

50. Chapter 44. The Automated Mapping of Plans for Plan Recognition

51. Chapter 45. A Logic for Default Reasoning About Probabilities

52. Chapter 46. Optimal Junction Trees

53. Chapter 47. From Influence Diagrams to Junction Trees

54. Chapter 48. Reduction of Computational Complexity in Bayesian Networks through Removal of Weak Dependences

55. Chapter 49. Using New Data to Refine a Bayesian Network

56. Chapter 50. Syntax-based default reasoning as probabilistic model-based diagnosis

57. Chapter 51. Induction of Selective Bayesian Classifiers

58. Chapter 52. Fuzzy Geometric Relations to Represent Hierarchical Spatial Information

59. Chapter 53. Constructing Belief Networks to Evaluate Plans

60. Chapter 54. Operator Selection While Planning Under Uncertainty

61. Chapter 55. Model-Based Diagnosis with Qualitative Temporal Uncertainty

62. Chapter 56. Incremental Dynamic Construction of Layered Polytree Networks

63. Chapter 57. Models of Consensus for Multiple Agent Systems

64. Chapter 58. A Probabilistic Calculus of Actions

65. Chapter 59. Robust Planning in Uncertain Environments

66. Chapter 60. Anytime Decision Making with Imprecise Probabilities

67. Chapter 61. Three Approaches to Probability Model Selection

68. Chapter 62. Knowledge Engineering for Large Belief Networks

69. Chapter 63. Solving Asymmetric Decision Problems with Influence Diagrams

70. Chapter 64. Belief Maintenance in Bayesian Networks

71. Chapter 65. Belief Updating by Enumerating High-Probability Independence-Based Assignments

72. Chapter 66. Global Conditioning for Probabilistic Inference in Belief Networks

73. Chapter 67. Belief Induced by the Partial Knowledge of the Probabilities

74. Chapter 68. Ignorance and the Expressiveness of Single- and Set-Valued Probability Models of Belief

75. Chapter 69. A probabilistic approach to hierarchical model-based diagnosis

76. Chapter 70. Semigraphoids are Two-Antecedental Approximations of Stochastic Conditional Independence Models

77. Chapter 71. Exceptional Subclasses in Qualitative Probability

78. Chapter 72. A Defect in Dempster-Shafer Theory

79. Chapter 73. State-Space Abstraction for Anytime Evaluation of Probabilistic Networks

80. Chapter 74. General Belief Measures

81. Chapter 75. Generating Graphoids from Generalised Conditional Probability

82. Chapter 76. On Axiomatization of Probabilistic Conditional Independencies

83. Chapter 77. Evidential Reasoning with Conditional Belief Functions

84. Chapter 78. Intercausal Independence and Heterogeneous Factorization

85. Author Index