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. RεS—A Relative Method for Evidential Reasoning

8. Chapter 2. Optimizing Causal Orderings for Generating DAGs from Data

9. Chapter 3. Modal Logics for Qualitative Possibility and Beliefs

10. Chapter 4. Structural Controllability and Observability in Influence Diagrams

11. Chapter 5. Lattice-Based Graded Logic: A Multimodal Approach

12. Chapter 6. Dynamic Network Models for Forecasting

13. Chapter 7. Reformulating Inference Problems Through Selective Conditioning

14. Chapter 8. Entropy and Belief Networks

15. Chapter 9. Parallelizing Probabilistic Inference Some Early Explorations

16. Chapter 10. Objection-Based Causal Networks

17. Chapter 11. A Symbolic Approach to Reasoning with Linguistic Quantifiers

18. Chapter 12. Possibilistic Assumption based Truth Maintenance System, Validation in a Data Fusion Application

19. Chapter 13. An entropy-based learning algorithm of Bayesian conditional trees

20. Chapter 14. Knowledge integration for conditional probability assessments

21. Chapter 15. Integrating Model Construction and Evaluation

22. Chapter 16. Reasoning With Qualitative Probabilities Can Be Tractable

23. Chapter 17. A computational scheme for reasoning in dynamic probabilistic networks

24. Chapter 18. The Dynamic of Belief in the transferable belief model and Specialization-Generalization Matrices

25. Chapter 19. A NOTE ON THE MEASURE OF DISCORD

26. Chapter 20. Semantics for Probabilistic Inference

27. Chapter 21. Some Problems for Convex Bayesians

28. Chapter 22. Bayesian Meta-Reasoning: Determining Model Adequacy from Within a Small World

29. Chapter 23. The Bounded Bayesian

30. Chapter 24. Representing Context-Sensitive Knowledge in a Network Formalism: A Preliminary Report

31. Chapter 25. A Probabilistic Network of Predicates

32. Chapter 26. Representing Heuristic Knowledge in D-S Theory

33. Chapter 27. The Topological Fusion of Bayes Nets

34. Chapter 28. Calculating Uncertainty Intervals From Conditional Convex Sets of Probabilities

35. Chapter 29. Sensor Validation using Dynamic Belief Networks

36. Chapter 30. Empirical Probabilities in Monadic Deductive Databases

37. Chapter 31. aHUGIN: A System Creating Adaptive Causal Probabilistic Networks

38. Chapter 32. MESA: Maximum Entropy by Simulated Annealing

39. Chapter 33. Decision Methods for Adaptive Task-Sharing in Associate Systems

40. Chapter 34. Modeling Uncertain Temporal Evolutions in Model-Based Diagnosis

41. Chapter 35. Guess-And-Verify Heuristics for Reducing Uncertainties in Expert Classification Systems

42. Chapter 36. R&D Analyst: An Interactive Approach to Normative Decision System Model Construction

43. Chapter 37. Possibilistic Constraint Satisfaction Problems or "How to handle soft constraints ?"

44. Chapter 38. Decision Making Using Probabilistic Inference Methods

45. Chapter 39. Conditional Independence in Uncertainty Theories

46. Chapter 40. The Nature of the unnormalized Beliefs encountered in the Transferable Belief Model

47. Chapter 41. Intuitions about Ordered Beliefs Leading to Probabilistic Models

48. Chapter 42. Expressing Relational and Temporal Knowledge in Visual Probabilistic Networks

49. Chapter 43. A Fuzzy Logic Approach to Target Tracking

50. Chapter 44. Towards Precision of Probabilistic Bounds Propagation

51. Chapter 45. An Algorithm for Deciding if a Set of Observed Independencies Has a Causal Explanation

52. Chapter 46. Generalizing Jeffrey Conditionalization

53. Chapter 47. INTERVAL STRUCTURE: A Framework for Representing Uncertain Information

54. Chapter 48. Exploring Localization In Bayesian Networks For Large Expert Systems

55. Chapter 49. A Decision Calculus for Belief Functions in Valuation-Based Systems

56. Chapter 50. Sidestepping the Triangulation Problem in Bayesian Net Computations

57. Author Index