Hostname: page-component-74656f9884-2dmjx Total loading time: 0 Render date: 2026-11-09T08:59:54.689Z Has data issue: false hasContentIssue false
  • English
  • Français

Qualitative frameworks for decision support: lessons from medicine

Published online by Cambridge University Press:  07 July 2009

John Fox  and
Paul Krause
John Fox
Affiliation:
Imperial Cancer Research Fund, Lincoln's Inn Fields, London WC2A 3PX, United Kingdom
Paul Krause
Affiliation:
Imperial Cancer Research Fund, Lincoln's Inn Fields, London WC2A 3PX, United Kingdom
Get access Rights & Permissions [Opens in a new window]

Abstract

Some weaknesses of current decision support technologies are discussed. Numerical methods have strong theoretical foundations but are representationally weak, and only deal with a small part of the decision process. Knowledge-based systems offer greater flexibility, but have not been accompanied by a clear decision theory. Theoretical development of symbolic decision procedures is advocated, an approach to the design of decision support systems based on first-order logic is presented, and work on this approach is reviewed. A central proposal is an extended form of inference called argumentation; reasoning qualitatively for and against decision options from generalized domain theories. Argumentation captures a natural and familiar form of reasoning, and contributes to the robustness, flexibility and intelligibility of problem solving, while having a clear theoretical basis. Argumentation was developed initially for medical applications though it may have much wider applicability.

Information

Type
Research Article
Information
The Knowledge Engineering Review , Volume 7 , Issue 1 , March 1992 , pp. 19 - 33
Copyright
Copyright © Cambridge University Press 1992

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.)

Article purchase

Temporarily unavailable

References

Andriole, SJ, 1989. Handbook of Decision Support Systems Tab Books.Google Scholar
Ayel, M and Laurent, J-P, eds., 1991. Validation, Verification and Test of Knowledge John Wiley.Google Scholar
Boden, M, 1989. Benefits and risks of knowledge-based systems, Report of Council for Science and Society, Oxford University Press.Google Scholar
Buchanan, BG and Smith, RG, 1988. “Fundamentals of Expert Systems” Ann. Rev. Comput. Sci. 3 23–58.CrossRefGoogle Scholar
Castles, FG, Murray, DJ and Potter, DC, eds., 1971. “Introduction” to Decisions, Organisations and Society Penguin.Google Scholar
Chard, T, 1991. “Qualitative probability versus quantitative probability in clinical diagnosis: a study using a computer simulation” Medical Decision Making 11 38–41.CrossRefGoogle ScholarPubMed
Cheeseman, P, 1985. “In defence of probability” In: Proceedings of 9th International Joint Conference on AIMorgan Kaufman,1002–1009.Google Scholar
Clark, DA, 1990. “Verbal uncertainty expressions: a. critical review of two decades of research” Current Psychology: Research and Reviews 9(3) 203–235.CrossRefGoogle Scholar
Coiera, E, 1992. “The qualitative representation of physical systems” The Knowledge Engineering Review 7(1).CrossRefGoogle Scholar
Dawes, RM, 1979. “The robust beauty of improper linear models in decision making” American Psychologist 34 571–582.CrossRefGoogle Scholar
de Dombal, FT, 1975. “Computer assisted diagnosis of Abdominal pain” In: Rose, J and Mitchell, JH, eds., Advances in Medical Computing Churchill Livingstone.Google Scholar
Fox, J, Barber, DC and Bardhan, KD, 1980. “Alternatives to Bayes: A quantitative comparison with rule-based diagnosis” Methods of Information in Medicine 19(4) 210–215.Google Scholar
Fox, J, 1990. “Automating assistance for safety critical decisions” Phil. Trans. Roy. Soc. B 327 555–567.Google ScholarPubMed
Fox, J, 1991. “Decision theory and autonomous systems” In: Singh, MG and Travé-Massuyès, L, eds., Proceedings of IMACS workshop on Decision Support Systems and Qualitative Reasoning North Holland.Google Scholar
Fox, J and Clarke, M, 1991. “Towards a formalisation of arguments in decision making” In: Proceedings of AAAI Spring Symposium on Argumentation and Belief AAAI.Google Scholar
Fox, J, Clark, DA, Glowinski, AJ and O'Neil, M, 1990a. “Using predicate logic to integrate qualitative reasoning and classical decision theory” IEEE Transactions on Systems, Man, and Cybernetics 20 347–357.Google Scholar
Fox, J and Krause, P, 1991. A formal basis for argumentation and practical reasoning, ICRF technical report.Google Scholar
Fox, J, Gordon, C, Glowsinski, AJ and O'Neil, M, 1990b. “Logic engineering for knowledge engineering: the Oxford system of medicine” Artificial Intelligence in Medicine 2 323–339.CrossRefGoogle Scholar
Gemigniani, MC, 1991. “Some legal aspects of expert systems” Expert Systems with Applications 2(4) 269–284.CrossRefGoogle Scholar
Kahneman, D, Slovic, P and Tversky, A, eds., 1982. Judgement Under Uncertainty: Heuristics and Biases Cambridge University Press.CrossRefGoogle Scholar
Keravnou, E and Washbrook, J, 1989. “What is a deep expert system?” The Knowledge Engineering Review 4(3) 205–233.CrossRefGoogle Scholar
Kruse, R and Siegel, P, eds., 1991. Symbolic and Quantitative Approaches to Uncertainty Springer-Verlag.CrossRefGoogle Scholar
Kulikowski, CA and Weiss, S, 1973. “An interactive facility for the inferential modelling of disease” Proc. Princeton Conf. on Information Sciences and Systems, 524.Google Scholar
Lauritzen, SL and Spiegelhalter, D, 1988. “Local computations with probablities on graphical structures and their application to expert systems” J Roy. Statist. Soc B 50 (2) 157–224.Google Scholar
Lindley, DV, 1985. Making Decisions (2nd ed) John Wiley.Google Scholar
O'Neil, M and Glowinski, A, 1990. “Evaluating and validating very large knowledge-based systems” Medical Information 15 237–251.Google ScholarPubMed
Parsons, S and Fox, J, 1991. “Qualitative and interval algebras for robust decision making under uncertainty” In: Singh, MG and Travé-Massuyès, L, eds., Proceedings of IMACS workshop on Decision Support Systems and Qualitative Reasoning North Holland.Google Scholar
Parsons, S, 1991. “Qualitative methods for integrating uncertainty handling formalisms”, Technical Report, Department of Electrical Engineering, Queen Mary and Westfield College, London.Google Scholar
Pearl, J, 1988. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, Morgan Kaufmann.Google Scholar
Pople, HE, 1975. “DIALOG: A model of diagnostic logic for internal medicine” Proc. IJCAI-77, 814–818.Google Scholar
Shanteau, J, 1987. “Psychological characteristics of expert decision makers” In: Mumpower, J, ed., Expert Judgement and Expert Systems vol F35, NATO ASI Series.Google Scholar
Shortliffe, EH, 1976. Computer-based medical consultations: MYCIN.Elsevier, New York.Google Scholar
Schwartz, S and Griffin, T, 1986. Medical Thinking: The psychology of medical judgement Springer-Verlag.CrossRefGoogle Scholar
Southwick, RW, 1991. “Explaining reasoning: an overview of explantion in knowledge based systems” The Knowledge Engineering Review 6(1) 1–20.CrossRefGoogle Scholar
Stenton, SP, 1987. “Dialogue management for co-operative knowledge based systems” The Knowledge Engineering Review 2(2) 99–122.CrossRefGoogle Scholar
Travé-Massuyès, L, 1992. “Qualitative reasoning over time: history and current prospects” The Knowledge Engineering Review 7(1).CrossRefGoogle Scholar
Wellman, MP, 1990. “Fundamental concepts of qualitative probabilistic networks” Artificial Intelligence 44 257–303.CrossRefGoogle Scholar
Wielinga, R, Schreiber, AT and Breuker, J, 1992. “KADS: A modelling approach to knowledge engineering” Knowledge Acquisition: Special issue on KADS (to appear).CrossRefGoogle Scholar
Wilson, M, Duce, D and Simpson, D, 1989. “Life cycles in software and knowledge engineering: a comparative review” The Knowledge Engineering Review 4(3) 189–204.CrossRefGoogle Scholar