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An overview of approaches to qualitative model construction

Published online by Cambridge University Press:  07 July 2009

Cis Schut  and
Bert Bredeweg
Cis Schut
Affiliation:
Department of Social Science informatics (S. W. 1.), University of Amsterdam, Roetersstraat 15, 1018 WB Amsterdam, The Netherlands., Email: schutswi.psy.uvo.nl
Bert Bredeweg
Affiliation:
Department of Social Science informatics (S. W. 1.), University of Amsterdam, Roetersstraat 15, 1018 WB Amsterdam, The Netherlands., Email: schutswi.psy.uvo.nl
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Abstract

In qualitative reasoning research, much effort has been spent on developing representation and reasoning formalisms. Only recently, the process of constructing models in terms of these formalisms has been recognised as an important research topic of its own. Approaches addressing this topic are examined in this review. For this purpose a general model of the task of constructing qualitative models is developed that serves as a frame of reference in considering these approaches. Two categories of approaches are identified: model composition and model induction approaches. The former compose a model from predefined partial models and the latter infer a model from behavioural data. Similarities and differences between the approaches are discussed using the general task model as a reference. It appears that the majority of approaches focus on automating model construction entirely. Assessing and debugging a model in cooperation with a modeller is identified as an important topic for future research

Type
Research Article
Information
The Knowledge Engineering Review , Volume 11 , Issue 1 , March 1996 , pp. 1 - 25
Copyright
Copyright © Cambridge University Press 1996

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References

Addanki, S, Cremonini, R and Penberthy, JS, 1991, “Graphs of models” Artificial Intelligence 51 145–177.Google Scholar
Bratko, I, Muggleton, S and Varsek, A, 1992, “Learning qualitative models of dynamic systems” In: Muggleton, S, editor, inductive Logic Programming, Academic Press, pp 437–452.Google Scholar
Bredeweg, B, 1992, “Expertise in qualitative prediction of behaviour” PhD thesis, University of Amsterdam.Google Scholar
Bredeweg, B and Schut, C, 1993, “Reducing ambiguity by learning assembly behaviour” In: Proceedings of the 13th International Joint Conference on Artificial Intelligence pp 980–985, Chambéry. France.Google Scholar
Cohn, AG, 1989, “Approaches to qualitative reasoning” Artificial Intelligence Review 3 177–232.CrossRefGoogle Scholar
Coiera, E, 1989, “Generating qualitative models from ex&e behaviour” Technical Report DCS 8901, Department of Computer Science, University of New South Wales.Google Scholar
Coiera, EW, 1992, “The qualitative representation of physical systems” The Knowledge Engineering Review 7 (1) 55–77.CrossRefGoogle Scholar
Crawford, J, Farquhar, A and Kuipers, B, 1990, “A compiler from physical models into qualitative differential equations” In: Proc. of AAAI-90, 365–372, Boston, MA.Google Scholar
de Kleer, JH, 1986, “An assumption-based TMS” Artificial Intelligence 28 127–162.Google Scholar
DeCoste, D, 1991, “Dynamic across-time measurement interpretation” Artificial Intelligence 51 273–341.Google Scholar
deKlcer, I and Brown, JS, 1984, “A qualitative physics based on confluences” Artificial Intelligence 247–83.Google Scholar
Falkenhainer, B, 1987, “Scientific theory formation through analogical inference” In: Proceedings of the 4th International Workshop on Machine Learning 218–229, Irvine, University of California.Google Scholar
Falkenhainer, B and Forbus, K, 1991, “Compositional modelling: Finding the right model for the job” Artificial Intelligence 51 95–143.Google Scholar
Falkenhainer, BC and Michalski, RS, 1986, “Integrating quantitative and qualitative discovery: the abacus system” Machine Learning 1 367–401.Google Scholar
Farquhar, A, 1993, “Automated Modeling of Physical Systems in the Presence of Incomplete Knowledge” PhD thesis, University of Texas, Austin.Google Scholar
Forbus, KD, 1983, “Measurement interpretation in qualitative process theory” In: Proc. of 8th IJCA1 315–320, Karlsruhe, FRG.Google Scholar
Forbus, KD, 1984, “Qualitative process theory” Artificial Intelligence 24 85–168.Google Scholar
KD, Forbus, 1986, “Interpreting measurements of physical systems” In: Proceedings of the AAAl 113–117, San Mateo, CA, Morgan Kaufmann.Google Scholar
Forbus, KD, 1988, “Qualitative physics: Past, present, and future” In: HE, Shrobe, editor, Exploring Artificial Intelligence 239–296, Morgan Kaufmann.Google Scholar
Forbus, KD, 1990, “The qualitative process engine” In: Weld, DS and deKleer, J, editors, Readings in Qualitative Reasoning about Physical Systems 220–235, Morgan Kaufmann.Google Scholar
Forbus, KD and Gentner, D, 1986, “Learning physical domains: Toward a theoretical framework” In: Michalski, RS, Carbonell, JG and Mitchell, TM, editors, Machine Learning: An Artificial intelligence Approach: Volume II 311–348, Morgan Kaufmann.Google Scholar
Goldberg, DE, editor, 1989, Genetic Algorithms in Search, Optimization, and Machine Learning Addison-Wesley.Google Scholar
Hau, DT and Coiera, EW, 1995, “Learning qualitative models of dynamic systems” Technical Report HPL 95–31, Hewlett Packard Laboratories.Google Scholar
Holland, JH, editor, 1975, Adaptation in Natural and Artificial Systems University of Michigan Press.Google Scholar
Iwasaki, Y, 1989, “Qualitative physics” In: Barr, A, Cohen, P and Feigenbuam, E, editors, The Handbook of Artificial Intelligence vol. IV 323–413, Addison-Wesley.Google Scholar
Iwasaki, Y and Levy, AY, 1993, “Automated model selection for simulation” In: Proceedings of the Seventh International Workshop on Qualitative Reasoning about Physical Systems 108–116, Seattle, WA.Google Scholar
Karnopp, DC, Margolis, DL and Rosenberg, RC, 1990, System Dynamics: a Unified Approach John Wiley & Sons, second revised edition.Google Scholar
Kuipers, B, 1986, “Qualitative simulation” Artificial Intelligence 29 289–388.Google Scholar
Kuipers, B, 1994, Qualitative Reasoning, Modelling and Simulation with incomplete Knowledge MIT Press.Google Scholar
Langley, P, Zytkow, JM, Simon, HA and Bradshaw, GL, 1986, “The search for regularity: Four aspects of scientific discovery” In: Michalski, RS, Carbonell, JG and Mitchell, TM, editors, Machine Learning: An Artificial intelligence Approach, volume 2 425–469, Morgan Kaufmann.Google Scholar
Levy, AY, Iwasaki, Y and Motoda, H, 1992, “Relevance reasoning to guide compositional modelling” In: Proceedings of the Sixth international Workshop on Qualitative Reasoning about Physical Systems 7–21, Edinburgh, Scotland, 08.Google Scholar
Low, CM and Iwasaki, Y, 1992, “Device modelling environment: an interactive environment for modelling device behaviour” Intelligent Systems Engineering 1(2)115–145, Winter.Google Scholar
Michalski, RS, 1983, “A theory and methodology of inductive learning” In: Michalski, RS, Carbonell, JG and Mitchell, TM, editors, Machine Learning: an Artificial Intelligence Approach Tioga.Google Scholar
Mozetič, I, 1987, “The role of abstractions in learning qualitative models” In: Proceedings of the 4th international Workshop on Machine Learning 242–255, Irvine, CA.Google Scholar
Mozetič, I, Bratko, I and Urbančič, T, 1990, “Varying levels of abstraction in qualitative modelling” In: Hayes, JE, Michie, D and Tyugu, E, editors, Machine intelligence 12 259–280, Clarendon Press.Google Scholar
Muggleton, SH and Feng, C, 1990, “Efficient induction of logic programs” In: Proceedings of the First Conference on Algorithmic Learning Theory Tokyo, Japan.Google Scholar
Nayak, PP, 1992, “Causal approximations” In: Proceedings of the Tenth National Conference on Artificial Intelligence AAAI-'92 703–709, San Jose, CA, 07.Google Scholar
Nayak, PP, Joskowicz, L and Addanki, S, 1992, “Automated model selection using context-dependent behaviors” In: Proceedings of the Tenth National Conference on Artificial Intelligence AAAI-'92 710–716, San Jose, CA, 07.Google Scholar
Nordhausen, B and Langley, P, 1993, “An integrated framework for empirical discovery” Machine Learning 12 17–47.Google Scholar
Paynter, HM, 1961, Analysis and Design of Engineering Systems MIT Press.Google Scholar
Richards, BL, Kraan, I and Kuipers, BJ, 1992, “Automatic abduction of qualitative models” In: Proceedings of the Tenth National Conference on Artificial Intelligence AAAI-'92 723–728, San Jose, CA, 07.Google Scholar
Rickel, J and Porter, B, 1992, “Automated modeling for answering prediction questions: Exploiting interaction paths” In: Proceedings of the Sixth International Workshop on Qualitative Reasoning about Physical Systems 82–95, Edinburgh, Scotland, 08.Google Scholar
Rickel, J and Porter, B, 1994, “Automated modeling for answering prediction questions: Selecting the time scale and system boundary” In: Proc. of AAAI-94 1191–1198, 07.Google Scholar
Rothcnbcrg, J, 1989, “The nature of modelling” In: Widman, LE, Loparo, KA and Nielson, NR, editors, Artificial intelligence, Simulation, and Modeling 75–92, John Wiley & Sons.Google Scholar
Schut, C and Bredeweg, B, 1993, “Automatic enhancement of model parsimony” In: Proceedings of the 7th international Workshop on Qualitative Reasoning 194–203, Seattle, WA, 05.Google Scholar
Schut, C and Bredeweg, B, 1994, “Supporting qualitative model specification In: Proc. of the Second International Conference on intelligent Systems Engineering 37–42, Technical University of HamburgHarburg, Germany.Google Scholar
Schut, C and Bredeweg, B, 1995, “Supporting qualitative model construction: Eliminating incorrectly predicted derivatives” In: Proceedings of the 9th International Workshop on Qualitative Reasoning 163–172, Amsterdam, The Netherlands, 05.Google Scholar
Shen, Q, Leitch, RR and Steele, AD, 1994, “A generic harness for the systematic generation of multiple models” In: Proc. of the Second International Conference on Intelligent Systems Engineering 49–55. Technical University of Hamburg-Harburg, Germany.Google Scholar
Top, J, 1993, “Conceptual Modelling of Physical Systems” PhD thesis, Twente University, 09.Google Scholar
Varek, A, 1991, “Qualitative model evolution” In: Proc. of the 12th IJCAl 1311–1316, Sydney, Australia.Google Scholar
Weld, DS, 1992, “Reasoning about model accuracy” Artificial Intelligence 56 255–300.Google Scholar
Weld, DS and deKleer, J, 1990, Readings in Qualitative Reasoning about Physical Systems Morgan Kaufmann.Google Scholar
Xia, S, Linkens, DA and Bennett, S, 1993, “Automatic modelling and analysis of dynamic physical systems using qualitative reasoning and bond graphs” Intelligent Systems Engineering 2 (3) 201–212, Autumn.Google Scholar
Zeigler, BP, 1976, Theory of Modelling and Simulation Wiley-Interscience.Google Scholar