The Knowledge Frontier

1. Front Matter

   Pages i-xxxv

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2. What Is Knowledge Representation?

   • Nick Cercone, Gordon McCalla

   Pages 1-43

3. Knowledge Representation: What’s Important About It?

   • William A. Woods

   Pages 44-79

4. Some Remarks on the Place of Logic in Knowledge Representation

   • David J. Israel

   Pages 80-91

5. Logic and Natural Language

   • Bonnie Lynn Webber

   Pages 92-102

6. Commonsense and Fuzzy Logic

   • L. A. Zadeh

   Pages 103-136

7. Basic Properties of Knowledge Base Systems

   • W. Marek

   Pages 137-160

8. First Order Logic and Knowledge Representation: Some Problems of Incomplete Systems

   • E. W. Elcock

   Pages 161-173

9. Admissible State Semantics for Representational Systems

   • Jon Doyle

   Pages 174-186

10. Accelerating Deductive Inference: Special Methods for Taxonomies, Colours and Times

    • Lenhart K. Schubert, Mary Angela Papalaskaris, Jay Taugher

    Pages 187-220

11. Knowledge Organization and Its Role in Temporal and Causal Signal Understanding: The ALVEN and CAA Projects

    • John K. Tsotsos, Tetsutaro Shibahara

    Pages 221-261

12. SNePS Considered as a Fully Intensional Propositional Semantic Network

    • Stuart C. Shapiro, William J. Rapaport

    Pages 262-315

13. Representing Virtual Knowledge Through Logic Programming

    • Veronica Dahl

    Pages 316-330

14. Theorist: A Logical Reasoning System for Defaults and Diagnosis

    • David Poole, Randy Goebel, Romas Aleliunas

    Pages 331-352

15. Representing and Solving Temporal Planning Problems

    • R. James Firby, Drew McDermott

    Pages 353-413

16. Analogical Modes of Reasoning and Process Modelling

    • Brian V. Funt

    Pages 414-428

17. Representing and Using Knowledge of the Visual World

    • William Havens, Alan Mackworth

    Pages 429-450

18. On Representational Aspects of VLSI- CADT Systems

    • Hassan Reghbati, Nick Cercone

    Pages 451-470

19. Back Matter

    Pages 471-512

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Knowledge representation is perhaps the most central problem confronting artificial intelligence. Expert systems need knowledge of their domain of expertise in order to function properly. Computer vlslOn systems need to know characteristics of what they are "seeing" in order to be able to fully interpret scenes. Natural language systems are invaluably aided by knowledge of the subject of the natural language discourse and knowledge of the participants in the discourse. Knowledge can guide learning systems towards better understanding and can aid problem solving systems in creating plans to solve various problems. Applications such as intelligent tutoring. computer-aided VLSI design. game playing. automatic programming. medical reasoning. diagnosis in various domains. and speech recogOltlOn. to name a few. are all currently experimenting with knowledge-based approaches. The problem of knowledge representation breaks down into several subsidiary problems including what knowledge to represent in a particular application. how to extract or create that knowledge. how to represent the knowledge efficiently and effectively. how to implement the knowledge representation scheme chosen. how to modify the knowledge in the face of a changing world. how to reason with the knowledge. and how tc use the knowledge appropriately in the creation of the application solution. This volume contains an elaboration of many of these basic issues from a variety of perspectives.

• artificial intelligence
• intelligence
• knowledge
• knowledge representation
• linguistics
• philosophy
• reason

• Computing Science Department, Simon Fraser University, Burnaby, Canada

  Nick Cercone

• Department of Computational Science, University of Saskatchewan, Saskatoon, Canada

  Gordon McCalla

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