Table of Content

1. Acknowledgements
2. Software
3. Preface
4. Representations and Methods
   • The Intelligent Computer
   • Semantic Nets and Description Matching
   • Generate and Test, Means-Ends Analysis, and Problem Reduction
   • Nets and Basic Search
   • Nets and Optimal Search
   • Trees and Adversarial Search
   • Rules and Rule Chaining
   • Rules, Substrates, and Cognitive Modeling
   • Frames and Inheritance
   • Fames and Commonsense
   • Numeric Constraints and Propagation
   • Symbolic Constraints and Propagation
   • Logic and Resolution Proof
   • Backtracking and Truth Maintenance
   • Planning
5. Learning and Regularity Recognition
   • Learning by Analyzing Difference
   • Learning by Explaining Experience
   • Learning by Correcting Mistakes
   • Learning by Recording Cases
   • Learning by Managing Multiple Models
   • Learning by Building Identification Trees
   • Learning by Training Neural Nets
   • Learning by Training Perceptions
   • Learning by Training Approximation Nets
   • Learning by Simulating Evolution
   • Recognizing Objects
   • Describing Images
   • Expressing Language Constrains
   • Responding to Questions and Commands
6. Appendices
   • Relational Databases
   • Exercises
7. Bibliography
8. Index
9. Colophon

Salient Features

• Semiformal representation and procedure specifications bring the ideas to within a step or two implementation and highlight unifying themes.
• Application examples provide a glimpse of the ideas at work in real-world systems.
• Powerful ideas and principles are identified ad emphasized.