Hostname: page-component-78c5997874-m6dg7 Total loading time: 0 Render date: 2024-11-18T21:53:37.932Z Has data issue: false hasContentIssue false
  • English
  • Français

A method to reduce ambiguities of qualitative reasoning for conceptual design applications

Published online by Cambridge University Press:  15 January 2013

Valentina D'Amelio ,
Magdalena K. Chmarra  and
Tetsuo Tomiyama
Valentina D'Amelio*
Affiliation:
Faculty of Mechanical, Maritime, and Materials Engineering, Department of Intelligent Mechanical Systems, Delft University of Technology, Delft, The Netherlands
Magdalena K. Chmarra
Affiliation:
Faculty of Mechanical, Maritime, and Materials Engineering, Department of Intelligent Mechanical Systems, Delft University of Technology, Delft, The Netherlands
Tetsuo Tomiyama
Affiliation:
Faculty of Mechanical, Maritime, and Materials Engineering, Department of Intelligent Mechanical Systems, Delft University of Technology, Delft, The Netherlands
*
Reprint requests to: Valentina D'Amelio, Faculty of Mechanical, Maritime, and Materials Engineering, Department of Intelligent Mechanical Systems, Delft University of Technology, Mekelweg 2, 2628 CD, Delft, The Netherlands. E-mail: valentina.damelio@gmail.com
Get access Rights & Permissions [Opens in a new window]

Abstract

Qualitative reasoning can generate ambiguous behaviors due to the lack of quantitative information. Despite many different research results focusing on ambiguities reduction, fundamentally it is impossible to totally remove ambiguities with only qualitative methods and to guarantee the consistency of results. This prevents the wide use of qualitative reasoning techniques in practical situations, particularly in conceptual design, where qualitative reasoning is considered intrinsically useful. To improve this situation, this paper initially investigates the origin of ambiguities in qualitative reasoning. Then it proposes a method based on intelligent interventions of the user who is able to detect ambiguities, to prioritize interventions on these ambiguities, and to reduce ambiguities based on the least commitment strategy. This interaction method breaks through the limit of qualitative reasoning in practical applications to conceptual design. The method was implemented as a new feature in a software tool called the Knowledge Intensive Engineering Framework in order to be tested and used for a printer design.

Keywords

Ambiguity ReductionConceptual DesignQualitative Process TheoryQualitative ReasoningUser Intervention
Type
Regular Articles
Information
AI EDAM , Volume 27 , Issue 1 , February 2013 , pp. 19 - 35
Copyright
Copyright © Cambridge University Press 2013

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

Adler, A. (2009). MIDOS: Multimodal interactive dialogue system. PhD Thesis. Massachusetts Institute of Technology.Google Scholar
Barr, A., Cohen, P.R., & Edward, A. (1989). The handbook of artificial intelligence. Artificial Intelligence 4, 325–338.Google Scholar
Bobrow, D.G. (1985). Qualitative Reasoning About Physical Systems. Amsterdam: Elsevier Science Publisher B.V.Google Scholar
Cohn, A.G. (1989). Approaches to qualitative reasoning. Artificial Intelligence 3, 177–232.Google Scholar
D'Ambrosio, B. (1987). Truth maintenance with numerous certainty estimates. Proc. 3rd Conf. AI Applications, pp. 244–249, Computer Society of the IEEE, Kissimmee, FL.Google Scholar
D'Ambrosio, B. (1989). Extending the mathematics in qualitative process theory. Artificial Intelligence, Simulation & Modeling (Widman, L.E., Loparo, K.A., & Nielsen, N.R., Eds.), pp. 133–158. New York: Wiley.Google Scholar
D'Amelio, V., Chmarra, M.K., & Tomiyama, T. (2011). Early design interference detection based on qualitative physics. Research in Engineering Design. Advance online publication. doi:10.1007/s00163-011-0108-7CrossRefGoogle Scholar
De Kleer, J. (1979). The origin and resolution of ambiguities in causal arguments. IJCAI-79, pp. 197–203. San Francisco, CA: Morgan Kaufmann.Google Scholar
De Kleer, J., & Bobrow, D.G. (1984). Qualitative reasoning with higher-order derivatives. Proc. AAAI, pp. 127–132. Los Altos, CA: Morgan Kaufmann.Google Scholar
De Kleer, J., & Brown, J.S. (1984). A qualitative physics based on confluences. Artificial Intelligence 24, 7–83.CrossRefGoogle Scholar
Eckert, C., Clarkson, P.J., & Zanker, W. (2004), Change and customization in complex engineering domains. Research in Engineering Design 15, 1–21.CrossRefGoogle Scholar
Forbus, K.D. (1981). Qualitative reasoning about physical processes. Proc. 7th Int. Joint Conf. Artificial Intelligence, pp. 326–330, Menlo Park, CA.Google Scholar
Forbus, K.D. (1984 a). Qualitative process theory. Artificial Intelligence 24, 85–168.CrossRefGoogle Scholar
Forbus, K.D. (1984 b). Qualitative Process Theory (Technical Report 789). Cambridge, MA: Massachusetts Institute of Technology, Artificial Intelligence Laboratory.Google Scholar
Forbus, K.D. (1998). Intelligent computer-aided engineering. AI Magazine 9(3), 23–36.Google Scholar
Forbus, K.D. (2011). Qualitative modeling. Cognitive Science 2(4), 374–391.Google ScholarPubMed
Forbus, K.D., & De Kleer, J. (1993). Building Problem Solvers. Cambridge, MA: MIT Press.Google Scholar
Kuipers, B. (1986). Qualitative simulation. Artificial Intelligence 29(3), 289–338.CrossRefGoogle Scholar
Kuipers, B. (1994). Qualitative Reasoning: Modeling and Simulation With Incomplete Knowledge. Cambridge, MA: MIT Press.Google Scholar
Kuipers, B., & Berleant, D. (1988). Using incomplete quantitative knowledge in qualitative reasoning. Proc. 7th National Conf. Artificial Intelligence, Menlo Park, CA.Google Scholar
Kuipers, B., & Chiu, C. (1987). Taming intractible branching in qualitative simulation. Proc. IJCAI. Los Altos, CA: Morgan Kaufmann.Google Scholar
Mavrovouniotis, M.L., & Stephanopoulos, G. (1989). Order of magnitude reasoning with O [M]. Artificial Intelligence Engineering 4(3), 106–114.CrossRefGoogle Scholar
Morgan, A.J. (1987). Predicting the behaviour of dynamic systems. Proc. AISB (Mellish, C., & Hallam, J., Eds). Chichester: Wiley.Google Scholar
Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Los Altos, CA: Morgan Kaufmann.Google Scholar
Price, C.J. (2000). AUTOSTEVE: Automated electrical design analysis. Proc. ECAI-2000, pp. 721–725. Amsterdam: IOS.Google Scholar
Price, C.J., Snooke, N.A., & Lewis, S.D. (2006). A layered approach to automated electrical safety analysis in automotive environments. Computers in Industry 57(5), 451–461.CrossRefGoogle Scholar
Price, C.J., Travé-Massuyès, L., Milne, R., Ironi, L., Forbus, K., Bredeweg, B., Lee, M.H., Struss, P., Snooke, N., Lucas, P., Cavazza, M., & Coghill, G.M. (2006). Qualitative futures. Knowledge Engineering Review 21(4), 317–334.CrossRefGoogle Scholar
Raiman, O. (1986). Order of magnitude reasoning. Proc. 5th National Conf. Artificial Intelligence, pp. 100–104.Google Scholar
Sandberg, M. (2007). Design for manufacturing: methods and applications using knowledge engineering. PhD Thesis. Luleå University of Technology.Google Scholar
Shen, Q., & Leitch, R. (1992). Integrating common-sense and qualitative simulation by the use of fuzzy sets. Recent Advances in Qualitative Physics (Faltings, B., & Struss, P., Eds.), pp. 83–100. Cambridge, MA: MIT Press.Google Scholar
Tomiyama, T., D'Amelio, V., Urbanic, J., & ElMaraghy, W. (2007). Complexity of multi-disciplinary design. CIRP Annals Manufacturing Technology 56(1), 89–92.CrossRefGoogle Scholar
Yoshioka, M. (2000). Knowledge Intensive Engineering Framework: KIEF (formerly known as SYSFUND) Manual. Accessed at http://www-kb.ist.hokudai.ac.jp/~yoshioka/KIEF/manual.pdfGoogle Scholar
Yoshioka, M., Umeda, Y., Takeda, H., Shimomura, Y., Nomaguchi, Y., & Tomiyama, T. (2004). Physical concept ontology for the Knowledge Intensive Engineering Framework. Advanced Engineering Informatics 18, 95–113.CrossRefGoogle Scholar