Hostname: page-component-cd9895bd7-gvvz8 Total loading time: 0 Render date: 2024-12-28T01:26:34.505Z Has data issue: false hasContentIssue false
  • English
  • Français

Knowledge representation for reasoning: Tables, frames, and rules in a cutting fluids application

Published online by Cambridge University Press:  27 February 2009

Laurence Moseley  and
Owain Dobson
Laurence Moseley
Affiliation:
Department of Computer Science, University of Wales, Swansea, Singleton Park, Swansea SA2 8PP, Wales
Owain Dobson
Affiliation:
Department of Computer Science, University of Wales, Swansea, Singleton Park, Swansea SA2 8PP, Wales
Get access Rights & Permissions [Opens in a new window]

Abstract

This paper describes decisions made during the development of an expert system for advising on problems that arise in the use of cutting fluids in engineering. It covers the problems of knowledge acquisition and of knowledge representation and of the relationship between them. The need for iterative prototyping is noted, and the choice between a database and a rule-based approach is discussed. The paper model and the machine model may not be isomorphic, although both are useful, albeit for different purposes.

Keywords

Knowledge-Based SystemFramesRulesDatabaseCutting Fluids
Type
Articles
Information
AI EDAM , Volume 10 , Issue 1 , January 1996 , pp. 37 - 45
Copyright
Copyright © Cambridge University Press 1996

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Bainbridge, L. (1986). Asking questions and accessing knowledge. Future Computing Systems 1, 143–149.Google Scholar
Boose, J.H., & Gaines, B.R. (1988). Knowledge Acquisition Tools for Expert Systems, Academic Press, London.Google Scholar
Chase, W.G., & Simon, H.A. (1973). Perception in chess. Cognitive Psychology 4, 55–81.CrossRefGoogle Scholar
de Groot, A.D. (1965). Thought and Choice in Chess. Mouton, The Hague.Google Scholar
Graham, I. (1994). Object Oriented Methods. 2nd Ed., pp. 49–50. Addison-Wesley, Wokingham, U.K.Google Scholar
Larkin, J.H. (1981). Enriching formal knowledge: A model for learning to solve textbook problems. In Cognitive Skills and Their Acquisition (Anderson, J.R., Ed). Erlbaum, Hillsdale, NJ.Google Scholar
Miller, G. (1956). The magical number seven, plus or minus two: some limits on our capacity for processing information. Psychological Review 63(2), 81–97.CrossRefGoogle ScholarPubMed
Mittal, S., & Dym, C.L. (1985). Knowledge acquisition from multiple experts. AI Magazine 6(2), 32–36.Google Scholar
Moseley, L.G., & Cartwright, M. (1992). The development of an expert system for operational use in the selection of industrial adhesives. Engineering Applications of Artificial Intelligence 5(4), 319–328.CrossRefGoogle Scholar
Nisbett, R.E., & Wilson, T.D. (1977). Telling more than we can know: verbal reports on mental processes. Psychological Review 84, 231–259.CrossRefGoogle Scholar
Parnas, D.L. (1994). Inspection of safety-critical software using program-function tables. IFIP Transactions A – Computer Science and Technology 53, 270–277.Google Scholar
Preece, A.D., & Moseley, L.G. (1992). Empirical study of expert systems development. Knowledge-Based Systems 5(2), 137–148.CrossRefGoogle Scholar
Tansley, D.W.S., & Hayball, C.C. (1993). Knowledge-Based Systems Analysis and Design: A KADS Developer’s Handbook. Prentice Hall, Hemel Hempstead, UK.Google Scholar