Hostname: page-component-7dd5485656-zklqj Total loading time: 0 Render date: 2025-10-30T05:44:06.472Z Has data issue: false hasContentIssue false
  • English
  • Français

Qualitative reasoning overtime: history and current prospects

Published online by Cambridge University Press:  07 July 2009

Louise Travé-Massuyès
Louise Travé-Massuyès
Affiliation:
Laboratoire d'Automatique et d'Analyse des Systèmes, Centre National de la Recherche Scientifique, 7, Avenue du Colonel Roche, 31077 Toulouse Cedex, France
Get access Rights & Permissions [Opens in a new window]

Abstract

This paper provides a historical summary of the motivations which have led several research communities to contemplate qualitative techniques. Qualitative reasoning satisfies various problem solving needs in high level decision tasks, embodied in a set of tools which allow deep knowledge to be put in compatible form with software requirements while still remaining realistic. An overview of these mathematical formalisms is presented; qualitative simulation is introduced as one of the most significant outcomes. Finally, some current research issues concerning temporal aspects of qualitative reasoning are discussed.

Information

Type
Research Article
Information
The Knowledge Engineering Review , Volume 7 , Issue 1 , March 1992 , pp. 1 - 18
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

Bousson, K and Travé-Massuyès, L, 1992a. “A theory of qualitative automata for dynamic process supervision”, Internal report LAAS-CNRS n.92007, Toulouse, France.Google Scholar
Bousson, K and Travé-Massuyès, L, 1992b. “Formalizing expertise qualitative operators”, Internal report LAAS-CNRS n.92009, Toulouse, France.Google Scholar
Caloud, P, 1988. Raisonnement Qualitatif: Application à l'aide à la Supervision des Procédés Contimus Thèse de Doctorat de l'INPG, Grenoble, France (in French).Google Scholar
Dague, P, 1988. “Order of magnitude revisited” 1st Qualitative Physics Workshop Paris, France.Google Scholar
De Kleer, J and Brown, JS, 1984. “A qualitative physics based on confluences” Artificial Intelligence 24 7–83.CrossRefGoogle Scholar
De Kleer, J and Williams, BC, 1987. “Diagnosing multiple faults”. Artificial Intelligence 32(1).CrossRefGoogle Scholar
Dormoy, JL and Raiman, O, 1988. “Assembling a device”. In: Proceedings ofAAAI-88, pp 330–335.Google Scholar
Dubois, D and Prade, H, 1988. “Fuzzy arithmetic in qualitative reasoning”. In: 3rd International Workshop “Bellman Continuum” Sofia-Antipolis, France.Google Scholar
Dubois, D and Prade, H, 1989. “Order of magnitude reasoning with fuzzy relations” Revue d'Intelligence Artificielle 3(4): 69–94.Google Scholar
Guerrin, F, 1990. “Biological process interpretation using a qualitative reasoning approach”. In: IMACS Annals on Computing & Applied Mathematics Proceedings Brussels, Belgium, VIII. A.3.1.–VIII. A.3.6.Google Scholar
Hayes, P, 1977. “The naive physics manifesto”. In: Michie, D, ed., Expert Systems in the Micro-electronic Age Edinburgh University Press.Google Scholar
Iwasaki, I, 1988. “Causal ordering in a mixed structure”. In: 2nd Qualitative Physics Workshop Paris, France.Google Scholar
Kuipers, BJ, 1986. “Qualitative simulation” Artificial Intelligence 29 289–338.CrossRefGoogle Scholar
Kuipers, BJ and Berleant, D, 1988. “Using incomplete quantitative knowledge in qualitative reasoning”. In: Proceedings AAAI-88.Google Scholar
Kuipers, BJ and Chiu, C, 1987. “Taming intractable branching in qualitative simulation”. In: Proceedings of IJCAI-87, pp 1079–1085.Google Scholar
Lancaster, K, 1962. “The scope of qualitative economics” Review of Economic Studies 29 99–123.CrossRefGoogle Scholar
Lunze, J, 1990. Qualitative Analysis of Dynamical Systems, Internal report ZfK-722, Zentralinstitut für Kernforschung, Rossendorf bei Dresden, Germany.Google Scholar
Mavrovouniotis, ML and Stephanopoulos, G, 1987. “Reasoning with order of magnitude and approximate relations Proceedings AAA1–87.Google Scholar
Maybee, J, 1981. “Sign solvability”. In: Greenberg, H and Maybee, J, eds., Computer Assisted Analysis and Model Simplification Academic Press.Google Scholar
Missier, A, 1991. Structures mathematiques pour le calcul qualitatif–Contribution à la simulation qualitative. Thèse de Doctorat de I'INSA, Toulouse, France (in French).Google Scholar
Missier, A, Pierà, N and Travé-Massuyès, L, 1989. “Order of magnitude qualitative algebras: a survey” Revue d'Intelligence Artificielle 3(4): 95–109.Google Scholar
Missier, A and Travé-Massuyès, L, 1991. “Temporal information in qualitative simulation” Conférence on AI, Simulation and Planning in High Autonomous SystemsCocoa Beach, FL.Google Scholar
Montmain, J, Leyval, L and Gentil, S, 1990. “On line qualitative interpretation of dynamic simulation for diagnosis”. In: Proceedings IMACS Workshop on Computing and Applied Mathematics.Google Scholar
MQ&D Project (Coordinator: Travé-Massuyès, L), 1991. Qualitative Reasoning: Methods, Tools and Applications Report of GR Automatique/AI, LAAS, Toulouse, France.Google Scholar
Pierà, N, Sanchez, M and Travé-Massuyès, L, 1991. “Qualitative operators for order of magnitude calculus: Robustness and Precision” In: 13th IMACS World Congress Dublin, Ireland.Google Scholar
Preprints, 1991. Workshop on Qualitative Reasoning about Physical Systems Genova, Italy.Google Scholar
Raiman, O, 1986. “Order of magnitude reasoning”. In: Proceedings AAAI-86 Philadelphia, PA.Google Scholar
Ritschard, G, 1983. “Computable qualitative comparative techniques” Econometrica 51 1145–1168.CrossRefGoogle Scholar
Sage, AP, 1990. “Decision making: information processing and organisational models”. In: Sage, AP, ed., Concise Encyclopedia of Information Processing in Systems and Organisations Pergamon, pp 96–103.Google Scholar
Shen, Q and Leitch, R, 1990. “A semi-quantitative extension to qualitative simulation”. In: Proceedings Avignon 90 Conference on Second Generation Expert SystemsAvignon, France.Google Scholar
Singh, MG, 1991. “Decision technologies for implementing business strategies in a competitive environment”. In: DSS&QR IMACS Workshop Toulouse, France.Google Scholar
Special volume on Qualitative Physics, 1984. Artificial Intelligence 24.Google Scholar
Thorn, R, 1977. Stabilité Structurelle et Morphogénèse InterEditions, Paris.Google Scholar
Struss, P and Dressler, O, 1989. “Physical negation—Integrating fault models into the general diagnostic engine”. In: Proceedings of the llth IJCAI Detroit, MI.Google Scholar
Travé, L and Dormoy, JL, 1988. “Qualitative calculus and applications”. In: IMACS Transactions on Scientific Computing '88 JC Baltzer AG.Google Scholar
Travé, L and Kaszkurewicz, E, 1986. “Qualitative controllability and observability of linear dynamical systems”. In: IFAC/IFOR Symposium on Large Scale Systems Pergamon.Google Scholar
Travé-Massuyès, L, 1989. “Qualitative analysis: Scope”. In: Singh, M., ed., Systems and Control Encyclopedia Pergamon, pp 473–481.Google Scholar
Travé-Massuyès, L, 1991. “Qualitive reasoning from different aspects and potential applications to decision support systems”. In: DSS&QR IMACS Workshop Toulouse, France.Google Scholar
Travé-Massuyès, L, Missier, A and Pierà, N, 1990. “Qualitative models for automatic control process supervision”. In: IFAC World Congress Tallinn.Google Scholar
Travé-Massuyès, L and Pierà, N, 1989. “Order of magnitude models as qualitative algebras: a survey”. In: Proceedings llth IJCAI Detroit, MI.Google Scholar
Vescovi, M, 1991. “Self-explanatory simulations from structural equations”. In: Proceedings IMACS DSS&QR Workshop Toulouse, France.Google Scholar