Uncertain Inference
Uncertain Inference
Choh Man Teng, Institute for Human and Machine Intelligence
Coping with uncertainty is a necessary part of ordinary life and is crucial to an understanding of how the mind works. It is a vital element in developing artificial intelligence that will not be undermined by its own rigidities. There have been many approaches to the problem of uncertain inference, ranging from probability to inductive logic to nonmonotonic logic. This book seeks to provide a clear exposition of these approaches within a unified framework.
- Detailed coverage of different approaches to uncertainty with extensive examples
- Will appeal to students in AI and computer science, as well as philosophy
- Kyburg is well-known in this area and author of many books (including KYBURG/Theory and Measurement/1984/0521 248787)
Reviews & endorsements
"Overall this book is one of the most thorough and objective treatments of inductive reasoning that I have encountered. It is clearly written with well chosen examples and both the scope and depth of the material covered is impressive...an excellent postgraduate textbook." Mathematical Reviews
Product details
January 2005Adobe eBook Reader
9780511032219
0 pages
0kg
This ISBN is for an eBook version which is distributed on our behalf by a third party.
Table of Contents
- Preface
- 1. Historical background
- 2. First order logic
- 3. The probability calculus
- 4. Interpretations of probability
- 5. Nonstandard measures of support
- 6. Nonmonotonic reasoning
- 7. Theory replacement
- 8. Statistical inference
- 9. Evidential probability
- 10. Semantics
- 11. Applications
- 12. Scientific inference.
