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Uncertainty in artificial intelligence 4 /
Clearly illustrated in this volume is the current relationship between Uncertainty and AI. It has been said that research in AI revolves around five basic questions asked relative to some particular domain: What knowledge is required? How can this knowledge be acquired? How can it be represented in...
| Clasificación: | Libro Electrónico |
|---|---|
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Amsterdam ; New York :
North-Holland,
1990.
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| Colección: | Machine intelligence and pattern recognition ;
v. 9. |
| Temas: | |
| Acceso en línea: | Texto completo Texto completo |
Tabla de Contenidos:
- Front Cover; Uncertainty in Artificial Intelligence 4; Copyright Page; PREFACE; Table of Contents; LIST OF CONTRIBUTORS; Section I: CAUSAL MODELS; CHAPTER 1. ON THE LOGIC OF CAUSAL MODELS; 1. INTRODUCTION AND SUMMARY OF RESULTS; 2. SOUNDNESS AND COMPLETENESS; 3. EXTENSIONS AND ELABORATIONS; ACKNOWLEDGMENT; REFERENCES; APPENDIX; Chapter 2. Process, Structure, and Modularity in Reasoning with Uncertainty; Abstract; 1 Introduction; 2 Related Research; 3 Hybrid Uncertainty Management; 4 Summary; References; Chapter 3. Probabilistic Causal Reasoning; Abstract; 1 Introduction; 2 Causal Theories.
- 3 Probabilistic Projection4 The Algorithm; 5 Acquiring Rules; 6 Conclusions; References; Chapter 4. Generating Decision Structures and Causal Explanations For Decision Making; ABSTRACT; 1. INTRODUCTION; 2. LEARNING A DECISION STRUCTURE; 3. CAUSAL EXPLANATION IN A DETERMINISTIC UNIVERSE WITH PERFECT INFORMATION; 4. CAUSAL EXPLANATION IN AN UNCERTAIN UNIVERSE; 5. TESTING THE THEORY; 6. CONCLUSIONS AND FUTURE RESEARCH; 7. ACKNOWLEDGEMENTS; REFERENCES; Chapter 5. Control of Problem Solving: Principles and Architecture; 1 Introduction; 2 Decision-Theoretic Selection; 3 The Architecture.
- 4 Conclusion5 Acknowledgements; References; CHAPTER 6. CAUSAL NETWORKS: SEMANTICS AND EXPRESSIVENESS; 1. INTRODUCTION; 2. UNDIRECTED GRAPHS; 3. DIRECTED-ACYCLIC GRAPHS (DAGS); 4. FUNCTIONAL DEPENDENCIES; 5. CONCLUSIONS; ACKNOWLEDGMENT; REFERENCES; Section II: UNCERTAINTY CALCULI AND COMPARISONS; Part 1: Uncertainty Calculi; CHAPTER 7. STOCHASTIC SENSITIVITY ANALYSIS USING FUZZY INFLUENCE DIAGRAMS; 1. INTRODUCTION AND OBJECTIVE; 2. BAYESIAN FUZZY PROBABILITIES : BASICS; 3. FUZZY PROBABILISTIC INFERENCE; 4. SOLVING DECISION PROBLEMS; 5. CONCLUSIONS; ACKNOWLEDGEMENTS; REFERENCES.
- CHAPTER 8. A LINEAR APPROXIMATION METHOD FOR PROBABILISTIC INFERENCE1. INTRODUCTION; 2. NOTATION AND BASIC FRAMEWORK; 3. VARIABLE TRANSFORMATIONS; 4. EXPERIMENTAL OBSERVATIONS; 5. LINEAR APPROXIMATION ALGORITHM; 6. CONCLUSIONS; ACKNOWLEDGEMENTS; REFERENCES; Chapter 9. Minimum Cross Entropy Reasoning in Recursive Causal Networks; 1 Introduction; 2 The Principle of Minimum Cross Entropy; 3 Recursive Causal Networks; 4 Reasoning with Multiple Uncertain Evidence; 5 Other Important Issues; 6 Conclusions; Acknowledgement; References; CHAPTER 10. PROBABILISTIC SEMANTICS AND DEFAULTS; 1. INTRODUCTION.
- 2. WHAT'S IN A DEFAULT?3. INFERENCE GRAPHS; 4. THE FAVOURS RELATION; 5. EXAMPLES; 6. CONCLUSIONS; ACKNOWLEDGEMENTS; REFERENCES; CHAPTER 11. Modal Logics of Higher-Order Probability; 1 Introduction; 2 Probability as a Modal Operator; 3 Flat Probability Models; 4 Coherence Principles; 5 Staged Probability Models; 6 Relation to Modal Logic; 7 Summary and Future Research; Acknowledgements; Notes; References; CHAPTER 12. A GENERAL NON-PROBABILISTIC THEORY OF INDUCTIVE REASONING; 1. INTRODUCTION; 2. THE THEORY; 3. A COMPARISON WITH PROBABILITY THEORY; 4. OTHER COMPARISONS; NOTES; REFERENCES.