Skip to main content Accessibility help
×
Home

Article contents

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

Published online by Cambridge University Press:  15 January 2013


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
Corresponding

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.


Type
Regular Articles
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.

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, 325338.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, 177232.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. 133158. 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. 197203. San Francisco, CA: Morgan Kaufmann.Google Scholar
De Kleer, J., & Bobrow, D.G. (1984). Qualitative reasoning with higher-order derivatives. Proc. AAAI, pp. 127132. Los Altos, CA: Morgan Kaufmann.Google Scholar
De Kleer, J., & Brown, J.S. (1984). A qualitative physics based on confluences. Artificial Intelligence 24, 783.CrossRefGoogle Scholar
Eckert, C., Clarkson, P.J., & Zanker, W. (2004), Change and customization in complex engineering domains. Research in Engineering Design 15, 121.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, 85168.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), 2336.Google Scholar
Forbus, K.D. (2011). Qualitative modeling. Cognitive Science 2(4), 374391.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), 289338.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), 106114.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. 721725. 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), 451461.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), 317334.CrossRefGoogle Scholar
Raiman, O. (1986). Order of magnitude reasoning. Proc. 5th National Conf. Artificial Intelligence, pp. 100104.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. 83100. 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), 8992.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, 95113.CrossRefGoogle Scholar

Full text views

Full text views reflects PDF downloads, PDFs sent to Google Drive, Dropbox and Kindle and HTML full text views.

Total number of HTML views: 5
Total number of PDF views: 40 *
View data table for this chart

* Views captured on Cambridge Core between September 2016 - 28th November 2020. This data will be updated every 24 hours.

Hostname: page-component-8465588854-kg8fp Total loading time: 0.546 Render date: 2020-11-28T08:10:44.586Z Query parameters: { "hasAccess": "0", "openAccess": "0", "isLogged": "0", "lang": "en" } Feature Flags last update: Sat Nov 28 2020 07:25:54 GMT+0000 (Coordinated Universal Time) Feature Flags: { "metrics": true, "metricsAbstractViews": false, "peerReview": true, "crossMark": true, "comments": true, "relatedCommentaries": true, "subject": true, "clr": false, "languageSwitch": true }

Send article to Kindle

To send this article to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about sending to your Kindle. Find out more about sending to your Kindle.

Note you can select to send to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

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

Send article to Dropbox

To send this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Dropbox.

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

Send article to Google Drive

To send this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Google Drive.

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

Reply to: Submit a response


Your details


Conflicting interests

Do you have any conflicting interests? *