Management of Knowledge Imperfection in Building Intelligent Systems

1. Front Matter

   PDF

2. “Classical” Expert Systems

   • Eugene Roventa, Tiberiu Spircu

   Pages 1-12

3. Knowledge Representation

   • Eugene Roventa, Tiberiu Spircu

   Pages 13-30

4. Uncertainty and Classical Theory of Probability

   • Eugene Roventa, Tiberiu Spircu

   Pages 31-87

5. Statistical Inference

   • Eugene Roventa, Tiberiu Spircu

   Pages 89-131

6. Bayesian (Belief) Networks

   • Eugene Roventa, Tiberiu Spircu

   Pages 133-152

7. Certainty Factors Theory

   • Eugene Roventa, Tiberiu Spircu

   Pages 153-160

8. Belief Theory

   • Eugene Roventa, Tiberiu Spircu

   Pages 161-186

9. Possibility Theory

   • Eugene Roventa, Tiberiu Spircu

   Pages 187-194

10. Approximate Reasoning

    • Eugene Roventa, Tiberiu Spircu

    Pages 195-232

11. Review

    • Eugene Roventa, Tiberiu Spircu

    Pages 233-246

12. Back Matter

    PDF

There are many good AI books. Usually they consecrate at most one or two chapters to the imprecision knowledge processing. To our knowledge this is among the few books to be entirely dedicated to the treatment of knowledge imperfection when bui- ing intelligent systems. We consider that an entire book should be focused on this important aspect of knowledge processing. The expected audience for this book - cludes undergraduate students in computer science, IT&C, mathematics, business, medicine, etc. , graduates, specialists and researchers in these fields. The subjects treated in the book include expert systems, knowledge representation, reasoning under knowledge Imperfection (Probability Theory, Possibility Theory, Belief Theory, and Approximate Reasoning). Most of the examples discussed in details throughout the book are from the medical domain. Each chapter ends with a set of carefully pe- gogically chosen exercises, which complete solution provided. Their understanding will trigger the comprehension of the theoretical notions, concepts and results. Chapter 1 is dedicated to the review of expert systems. Hence are briefly discussed production rules, structure of ES, reasoning in an ES, and conflict resolution. Chapter 2 treats knowledge representation. That includes the study of the differences between data, information and knowledge, logical systems with focus on predicate calculus, inference rules in classical logic, semantic nets and frames.

• Approximate Reasoning
• Building Intelligent Systems
• Bézier Curves
• Knowledge Imperfection
• STATISTICA
• Uncertain Reasoning
• artificial intelligence
• expert system
• knowledge
• knowledge representation

• York University, Canada

  Eugene Roventa

• “Carol Davila” University of Medicine and Pharmacy , Romania

  Tiberiu Spircu

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