Hostname: page-component-cd9895bd7-dk4vv Total loading time: 0 Render date: 2024-12-26T19:50:41.277Z Has data issue: false hasContentIssue false
  • English
  • Français

The qualitative representation of physical systems

Published online by Cambridge University Press:  07 July 2009

Enrico Coiera
Enrico Coiera
Affiliation:
Hewlett-Packard Laboratories, Filton Rd., Stoke Gifford, Bristol BS12 6QZ, UK
Get access Rights & Permissions [Opens in a new window]

Abstract

The representation of physical systems using qualitative formalisms is examined in this review, with an emphasis on recent developments in the area. The push to develop reasoning systems incorporating deep knowledge originally focused on naive physical representations, but has now shifted to more formal ones based on qualitative mathematics. The qualitative differential constraint formalism used in systems like QSIM is examined, and current efforts to link this to competing representations like Qualitative Process Theory are noted. Inference and representation are intertwined, and the decision to represent notions like causality explicitly, or infer it from other properties, has shifted as the field has developed. The evolution of causal and functional representations is thus examined. Finally, a growing body of work that allows reasoning systems to utilize multiple representations of a system is identified. Dimensions along which multiple model hierarchies could be constructed are examined, including mode of behaviour, granularity, ontology, and representational depth.

Type
Research Article
Information
The Knowledge Engineering Review , Volume 7 , Issue 1 , March 1992 , pp. 55 - 77
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.)

References

Abu-Hanna, A and Gold, Y, 1990. “Adaptive, multilevel diagnosis and modelling of dynamic systems” International Journal of Expert Systems 3 1–30.Google Scholar
Addanki, S, Cremonini, R and Penberthy, SJ, 1989. “Reasoning about assumptions in graphs of models”. In: Proceedings of the 11th International Joint Conference on Artificial Intelligence, pp 1432–1438.Google Scholar
Addanki, S, Cremonini, R and Penberthy, SJ, 1989. “Contexts: dynamic identification of common parameters in distributed analysis of complex devices”. In: Proceedings of the 11th International Joint Conference on Artificial Intelligence, pp 104–109.Google Scholar
Bobrow, D. (ed.), 1984. Qualitative Reasoning About Physical Systems MIT Press.CrossRefGoogle Scholar
Bratko, I, Mozetic, I and Lavrac, N, 1989. Kardio—A Study in Deep and Qualitative Knowledge for Expert Systems MIT Press.Google Scholar
Brooks, R, 1991. “Intelligence without representation” Artificial Intelligence 47 139–159.CrossRefGoogle Scholar
Bunge, M, 1979. Causality and Modern Science Dover Publications.Google Scholar
Bylander, T, 1987. “Using consolidation for reasoning about devices” Technical Report, Laboratory for Artificial Intelligence Department of Computer and Information Science, Ohio State University.Google Scholar
Bylander, T, 1988. “A critique of qualitative simulation from a consolidation viewpoint” IEEE Transactions on Systems, Man, and Cybernetics 18 252–263.Google Scholar
Bylander, T, 1990. “Some causal models are deeper than others” Artificial Intelligence in Medicine 2 123–128.Google Scholar
Chandrasekaran, B, 1983. “Towards a taxonomy of problem solving types” The AI Magazine Winter/Spring, pp 9–17.Google Scholar
Chiang, AC, 1984. Fundamental Methods of Mathematical Economics McGraw-Hill.Google Scholar
Chiu, C, 1988. “Higher order derivative constraints and a QSIM-based total simulation scheme” Technical Report AITR88–65, The University of Texas at Austin, Artificial Intelligence Laboratory.Google Scholar
Cohn, AG, 1989. “Approaches to qualitative reasoning” Artificial Intelligence Review 3 177–232.CrossRefGoogle Scholar
Coiera, EW, 1989. “Reasoning with qualitative disease histories for diagnostic patient monitoring” Ph.D. Thesis, Department of Computer Science, University of New South Wales.Google Scholar
Coiera, EW, 1990. “Monitoring diseases with empirical and model generated histories” Artificial Intelligence in Medicine 2 135–147.CrossRefGoogle Scholar
Coiera, EW, 1992. “Qualitative superposition” Artificial Intelligence (to appear).CrossRefGoogle Scholar
Collins, JW and Forbus, KD, 1987. “Reasoning about fluids via molecular collections”. In: Proceedings of AAAI-87, pp 590–595.Google Scholar
Compton, P and Jansen, R, 1990. “A philosophical basis for knowledge acquisition” Knowledge Acquisition 2 241–257.Google Scholar
Crawford, J, Farqhuar, A and Kuipers, B, 1990. “QPC: A compiler from physical models into qualitative differential equations”. In: Proceedings of AAAI-90, pp 365–372.Google Scholar
Davis, R, 1984. “Diagnostic reasoning based on structure and behaviour” Artificial Intelligence 24 347–410.CrossRefGoogle Scholar
Davis, R, 1987. “Robustness and transparency in intelligent systems”. In: Proceedings of the Third Australian Conference on the Applications of Expert Systems, pp 143–164.Google Scholar
de Kleer, J and Brown, JS, 1984. “A qualitative physics based on confluences” Artificial Intelligence 24 7–83.Google Scholar
de Kleer, J and Brown, JS, 1986. “Theories of causal ordering” Artificial Intelligence 29 33–61.Google Scholar
Fishwick, PA, 1989. “A study of terminology and issues in qualitative simulation” Simulation 52 5–9.Google Scholar
Fishwick, PA, 1989b. “Qualitative methodology in simulation model engineering” Simulation 52 95–101.Google Scholar
Fishwick, PA and Zeigler, B, 1990. “Qualitative physics: Towards the automation of systems problem solving”. In: Proceedings on AI, Simulation and Planning in High Autonomy Systems IEEE Computer Society Press, pp 118–134.Google Scholar
Forbus, KD, 1984. “Qualitative process theory” Artificial Intelligence 24 85–168.Google Scholar
Forbus, KD, 1988. “Commonsense physics: A review” Annual Review of Computer Science 3 197–232.CrossRefGoogle Scholar
Forbus, KD, 1990. “The qualitative process engine”. In: Weld, D, and de Kleer, J (eds.), Readings in Qualitative Reasoning about Physical Systems Morgan Kaufmann.Google Scholar
Forbus, KD and Gentner, D, 1990. “Causal reasoning about quantities”. In: Weld, D and de Kleer, J (eds.), Readings in Qualitative Reasoning about Physical Systems Morgan Kaufmann, pp 686–677.Google Scholar
Gandolfo, G, 1981. Methematical Methods and Models in Economics North-Holland.Google Scholar
Hamscher, W and Davis, R, 1987. “Issues in model based troubleshooting” AI Memo 893, Artificial Intelligence Laboratory, Massachusetts Institute of Technology.Google Scholar
Hart, PE, 1982. “Directions for AI in the eighties” SIGART 79, pp 11–16.CrossRefGoogle Scholar
Hayes, PJ, 1979. “The naive physics manifesto”. In: Michie, D (ed.), Expert Systems in the Microelectronic Age Edinburgh University Press.Google Scholar
Hayes, PJ, 1985. “Naive physics 1: Ontology for liquids”. In: JR Hobbs and RC Moore (eds.), pp 71–89. (Reprinted in Weld, D and de Kleer, J, Expert Systems in the Microelectronic Age Edinburgh University Press)Google Scholar
Hobbs, JR and Moore, RC (eds.), 1985. Formal Theories of the Commonsense World Ablex.Google Scholar
Ironi, L, Stefanelli, M and Lanzola, G, 1990. “Qualitative models in medical diagnosis” Artificial Intelligence in Medicine 2 85–101.CrossRefGoogle Scholar
Iwasaki, Y and Simon, HA, 1986. “Causality in device behaviour” Artificial Intelligence 29 3–32.CrossRefGoogle Scholar
Iwasaki, Y and Simon, HA, 1986. “Theories of causal ordering: reply to de Kleer and Brown” Artificial Intelligence 29 63–72.CrossRefGoogle Scholar
Iwasaki, Y, 1987. “Generating behaviour equations from explicit representations of mechanisms” Carnegie Mellon University, Computer Science Department Report CMU-CS-87–131.Google Scholar
Iwasaki, Y, 1988. “Causal ordering in a mixed structure”. In: Proceedings of AAAI-88, pp 313–318.Google Scholar
Iwasaki, Y, 1990. “Reasoning with Multiple Abstraction Models” Knowledge Systems Laboratory Report No. KSL 90–52, Stanford University, August.Google Scholar
Iwasaki, Y, 1990. “On the relationship between model abstraction and causality: Variance of causal ordering under abstraction operations” Knowledge Systems Laboratory Report No. KSL 90–59, Stanford University, September.Google Scholar
Keravnou, E and Washbrook, J, 1989. “Deep and shallow models in medical expert systems” Artificial Intelligence in Medicine 111–28.Google Scholar
Keuneke, A and Allemang, D, 1989. “Exploring the no-function-in-structure principle” Journal of Experimental and Theoretical Artificial Intelligence 1 79–89.Google Scholar
Kirsch, D, 1991. “Today the earwig, tomorrow man?” Artificial Intelligence 47 161–184.Google Scholar
Klein, D and Finin, T, 1987. “What's in a deep model? In: Proceedings of the 10th International Joint Conference on Artificial Intelligence, pp 559–562.Google Scholar
Kuipers, B, 1986. “Qualitative simulation” Artificial Intelligence 29 289–338.Google Scholar
Kuipers, B and Chiu, C, 1987. “Taming intractable branching in qualitative simulation”. In: Proceedings of the 10th International Joint Conference on Artificial Intelligence pp 1079–1085.Google Scholar
Kuipers, B, 1987. “Abstraction by time-scale in qualitative simulation”. In: Proceedings of AAAI-87 pp 621–625.Google Scholar
Kuipers, B, 1987b. “Qualitative simulation as causal explanation” IEEE Transactions on Systems Man and Cybernetics 17 432–444.Google Scholar
Kuipers, B and Berleant, D, 1988. “Using incomplete quantitative knowledge in qualitative reasoning” Proceedings of AAAI-88.Google Scholar
Lee, W and Kuipers, B, 1988. “Non-intersection of trajectories in qualitative phase space: A global constraint for qualitative simulation”. In: Proceedings of AAAI-88 pp. 286–296.Google Scholar
Lenat, DB and Feigenbaum, EA, 1988. “On the thresholds of knowledge”. In: Proceedings Fourth Australian Conference on the Applications of Expert Systems, pp 31–56. (Revised version reprinted in Artificial Intelligence 47 (1991) 185–250.)Google Scholar
Levesque, H and Brachman, R, 1984. “A fundamental tradeoff in knowledge representation and reasoning”. In: Proceedings of CSSl-84, London, Ontario, pp 141–152. (Reprinted in Readings in Knowledge Representation, R Brachman and H Levesque (eds.), Morgan Kaufmann 1985) 41–70.Google Scholar
Liu, Z and Farley, A, 1990. “Shifting ontological perspectives in reasoning about physical systems”. In: Proceedings of AAAI-90, pp 395–400.Google Scholar
Long, W, 1983. “Reasoning about state from causation and time in a medical domain”. In: Proceedings of AAAI-83, pp 251–254.Google Scholar
Morris, M, 1991. “Why there are no mental representations” Minds and Machines 11–30.Google Scholar
McDermott, D, 1987. “Logic, problem solving, and deduction” Annual Review of Computer Science 2 187–229.CrossRefGoogle Scholar
Pan, J, 1984. “Qualitative reasoning with deep-level mechanism models for diagnoses of mechanism failures”. In: Proceedings ofCAIA-84, pp 295–301.Google Scholar
Patil, RS, 1981. “Causal representation of patient illness for electrolyte and acid-base diagnosis” Ph.D. Thesis, Massachusetts Institute of Technology.Google Scholar
Rosch, E, 1978. “Principles of classification”. In: Rosch, E and Lloyd, B (eds.), Cognition and Categorization, Erlbaum, 27–48. (Reprinted in Readings in Cognitive Science, Morgan Kaufmann, 1988), pp 312–322.Google Scholar
Söderman, U and Strömberg, J, 1991. “Combining qualitative and quantitative knowledge to generate models of physical systems”. In: Proceedings of the 12th International Joint Conference on Artificial Intelligence, pp 1158–1163.Google Scholar
Sticklen, J, 1991. “Functional reasoning and functional modelling” IEEE Expert 6 20–21.Google Scholar
Struss, P, 1988. “Global filters for qualitative behaviours”. In: Proceedings of AAAI-88, pp 275–279.Google Scholar
Top, J and Akkermans, H, 1990. Bond-Graph Based Reasoning about Physical Systems, Working Papers of the 1990 Workshop on Model Based Reasoning, Boston.Google Scholar
Top, J and Akkermans, H, 1991. “Computational and physical causality”. In: Proceedings of the 12th International Joint Conference on Artificial Intelligence, pp 1171–1176.Google Scholar
Weiss, S, Kulikowski, C et al. , 1978. “A model-based method for computer-aided medical decision making” Artificial Intelligence 11 145–172.Google Scholar
Weld, D, 1986. “The use of aggregation in causal simulation” Artificial Intelligence 30 1–34.Google Scholar
Weld, D, 1988. “Comparative analysis” Artificial Intelligence 36 333–373.Google Scholar
Weld, D, 1988. “Exaggeration”. In: Proceedings of AAAI-88, pp 291–295.Google Scholar
Weld, D and de Kleer, J, 1990. Readings in Qualitative Reasoning About Physical Systems Morgan Kaufmann.Google Scholar
Weld, D, 1990. “Approximation reformulations”. In: Proceedings of AAAI-90, pp 407–412.Google Scholar
Weld, D and Addanki, S, 1991. “Query-directed approximation”. In: Fallings, B and Struss, P (eds.), Recent Advances in Qualitative Physics, MIT Press.Google Scholar
Wellman, M, 1990. “Fundamental concepts of qualitative probabilistic networks” Artificial Intelligence 44 257–303.CrossRefGoogle Scholar
Williams, B, 1988. “MINIMA—A symbolic approach to qualitative algebraic reasoning”. In: Proceedings of AAAI-88, pp 264–269.Google Scholar