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Case-based reasoning and system design: An integrated approach based on ontology and preference modeling

Published online by Cambridge University Press:  20 January 2014

Juan Camilo Romero Bejarano ,
Thierry Coudert ,
Elise Vareilles ,
Laurent Geneste ,
Michel Aldanondo  and
Joël Abeille
Juan Camilo Romero Bejarano
Affiliation:
Axsens, Toulouse, France Ecole Nationale d'Ingenieurs de Tarbes, University of Toulouse, Tarbes, France
Thierry Coudert*
Affiliation:
Ecole Nationale d'Ingenieurs de Tarbes, University of Toulouse, Tarbes, France
Elise Vareilles
Affiliation:
Mines-Albi, University of Toulouse, Toulouse, France
Laurent Geneste
Affiliation:
Ecole Nationale d'Ingenieurs de Tarbes, University of Toulouse, Tarbes, France
Michel Aldanondo
Affiliation:
Mines-Albi, University of Toulouse, Toulouse, France
Joël Abeille
Affiliation:
Ecole Nationale d'Ingenieurs de Tarbes, University of Toulouse, Tarbes, France
*
Reprint requests to: Thierry Coudert, ENIT, 47 Avenue d'azereix, 65016 Tarbes Cedex, France. E-mail: thierry.coudert@enit.fr
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Abstract

This paper addresses the fulfillment of requirements related to case-based reasoning (CBR) processes for system design. Considering that CBR processes are well suited for problem solving, the proposed method concerns the definition of an integrated CBR process in line with system engineering principles. After the definition of the requirements that the approach has to fulfill, an ontology is defined to capitalize knowledge about the design within concepts. Based on the ontology, models are provided for requirements and solutions representation. Next, a recursive CBR process, suitable for system design, is provided. Uncertainty and designer preferences as well as ontological guidelines are considered during the requirements definition, the compatible cases retrieval, and the solution definition steps. This approach is designed to give flexibility within the CBR process as well as to provide guidelines to the designer. Such questions as the following are conjointly treated: how to guide the designer to be sure that the requirements are correctly defined and suitable for the retrieval step, how to retrieve cases when there are no available similarity measures, and how to enlarge the research scope during the retrieval step to obtain a sufficient panel of solutions. Finally, an example of system engineering in the aeronautic domain illustrates the proposed method. A testbed has been developed and carried out to evaluate the performance of the retrieval algorithm and a software prototype has been developed in order to test the approach. The outcome of this work is a recursive CBR process suitable to engineering design and compatible with standards. Requirements are modeled by means of flexible constraints, where the designer preferences are used to express the flexibility. Similar solutions can be retrieved even if similarity measures between features are not available. Simultaneously, ontological guidelines are used to guide the process and to aid the designer to express her/his preferences.

Keywords

Case-Based ReasoningDesignOntologyPreferencesRetrievalSystem Engineering
Type
Regular Articles
Information
AI EDAM , Volume 28 , Issue 1 , February 2014 , pp. 49 - 69
Copyright
Copyright © Cambridge University Press 2014 

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References

REFERENCES

Aamodt, A., & Plaza, E. (1994). Case-based reasoning: foundational issues, methodological variations, and system approaches. AI Communications 7(1), 39–52.Google Scholar
Abeille, J., Coudert, T., Vareilles, É., Geneste, L., Aldanondo, M., & Roux, T. (2010). Formalization of an integrated system/project design framework: first models and processes. In Complex Systems and Management (Aiguier, M., Bretaudeau, F., & Krob, D., Eds.), pp. 207–217. Berlin: Springer.Google Scholar
Althoff, K.-D., & Weber, R. (2005). Knowledge management in case-based reasoning. Knowledge Engineering Review 20(3), 305–310.Google Scholar
Altshuller, G. (1996). And Suddenly the Inventor Appeared: Triz, the Theory of Inventive Problem Solving. Worcester, MA: Technical Innovation Center.Google Scholar
Armaghan, N., & Renaud, J. (2012). An application of multi-criteria decision aids models for case-based reasoning. Information Sciences 210, 55–66.Google Scholar
Avramenko, Y., & Kraslawski, A. (2006). Similarity concept for case-based design in process engineering. Computers & Chemical Engineering 30(3), 548–557.CrossRefGoogle Scholar
Batet, M., Sánchez, D., & Valls, A. (2011). An ontology-based measure to compute semantic similarity in biomedicine. Journal of Biomedical Informatics 44(1), 118–125.Google Scholar
Benferhat, S., Dubois, D., Kaci, S., & Prade, H. (2006). Bipolar possibility theory in preference modeling: representation, fusion and optimal solutions. Information Fusion 7(1), 135–150.Google Scholar
Bergmann, R. (2002). Experience Management: Foundations, Development Methodology, and Internet-Based Applications. Berlin: Springer.Google Scholar
Brandt, S.C., Morbach, J., Miatidis, M., Theißen, M., Jarke, M., & Marquardt, W. (2008). An ontology-based approach to knowledge management in design processes. Computers & Chemical Engineering 32(1–2), 320–342.Google Scholar
Cao, D., Li, Z., & Ramani, K. (2011). Ontology-based customer preference modeling for concept generation. Advanced Engineering Informatics 25(2), 162–176.Google Scholar
Chandrasegaran, S.K., Ramani, K., Sriram, R.D., Horvth, I., Bernard, A., Harik, R.F., & Gao, W. (2013). The evolution, challenges, and future of knowledge representation in product design systems. Computer-Aided Design 45(2), 204–228.Google Scholar
Chang, X., Sahin, A., & Terpenny, J. (2008). An ontology-based support for product conceptual design. Robotics and Computer-Integrated Manufacturing 24(6), 755–762.Google Scholar
Chen, X., Gao, S., Guo, S., & Bai, J. (2012). A flexible assembly retrieval approach for model reuse. Computer-Aided Design 44(6), 554–574.Google Scholar
Chen, Y.-J., Chen, Y.-M., Chu, H.-C., & Kao, H.-Y. (2008). On technology for functional requirement-based reference design retrieval in engineering knowledge management. Decision Support Systems 44(4), 798–816.Google Scholar
Chenouard, R., Granvilliers, L., & Sebastian, P. (2009). Search heuristics for constraint-aided embodiment design. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 23, 175–195.Google Scholar
Cordi, V., Lombardi, P., Martelli, M., & Mascardi, V. (2005). An ontology-based similarity between sets of concepts. In Proc. Workshop dagli Oggetti agli Agenti (WOA) (Corradini, F., Paoli, F.D., Merelli, E., & Omicini, A., Eds.), pp. 16–21. Bologna: Pitagora Editrice.Google Scholar
Cordier, A., Mascret, B., & Mille, A. (2009). Extending case-based reasoning with traces. In Grand Challenges for Reasoning from Experiences, workshop at IJCAI'09.Google Scholar
Cortes-Robles, G., Negny, S., & Le-Lann, J.M. (2009). Case-based reasoning and TRIZ: a coupling for innovative conception in chemical engineering. Chemical Engineering and Processing: Process Intensification 48(1), 239–249.Google Scholar
Coudert, T., Vareilles, É., Aldanondo, M., Geneste, L., & Abeille, J. (2011). Synchronization of system design and project planning: integrated model and rules. 5th IEEE Int. Conf. Software, Knowledge, Information, Industrial Management and Applications (SKIMA' 2011), pp. 1–6.Google Scholar
Coudert, T., Vareilles, É., Geneste, L., Aldanondo, M., & Abeille, J. (2011). Proposal for an integrated case based project planning. In Complex Systems Design and Management (Hammami, O., Krob, D., & Voirin, J.-L., Eds.), pp. 133–144. Berlin: Springer.Google Scholar
Dalkir, K. (2005). Knowledge Management in Theory and Practice. Amsterdam: Elsevier/Butterworth Heinemann.Google Scholar
Darlington, M.J., & Culley, S.J. (2008). Investigating ontology development for engineering design support. Advanced Engineering Informatics 22(1), 112–134.Google Scholar
Dieter, G. (2000). Engineering Design: A Materials and Processing Approach. New York: McGraw–Hill.Google Scholar
Domshlak, C., Hüllermeier, E., Kaci, S., & Prade, H. (2011). Preferences in AI: an overview. Artificial Intelligence 175(7–8), 1037–1052.Google Scholar
Dubois, D., Esteva, F., Garcia, P., Godo, L., de Mantaras, R.L., & Prade, H. (1997). Fuzzy modelling in case-based reasoning and decision. Proc. ICCBR-97, Case-Based Reasoning Research and Development (Leake, D.B., & Plaza, E., Eds.), pp. 599–610. New York: Springer–Verlag.Google Scholar
Dubois, D., Fargier, H., & Prade, H. (1996). Possibility theory in constraint satisfaction problems: handling priority, preference and uncertainty. Applied Intelligence 6(4), 287–309.Google Scholar
Dubois, D., Prade, H., Esteva, F., Garcia, P., Godo, L., & Lopez de Mantaras, R. (1998). Fuzzy set modelling in case-based reasoning. International Journal of Intelligent Systems 13(4), 345–373.Google Scholar
Faure, A., & Bisson, G. (1999). Modeling the experience feedback loop to improve knowledge base reuse in industrial environment. In 12th Workshop on Knowledge Acquisition, Modeling and Management, KAW 99. Banff, Canada.Google Scholar
Finnie, G.R., & Sun, Z. (2003). R5 model for case-based reasoning. Knowledge-Based Systems 16(1), 59–65.Google Scholar
Foguem, B.K., Coudert, T., Béler, C., & Geneste, L. (2008). Knowledge formalization in experience feedback processes: an ontology-based approach. Computers in Industry 59(7), 694–710.Google Scholar
Gao, C., Huang, K., Chen, H., & Wang, W. (2006). Case-based reasoning technology based on TRIZ and generalized location pattern. Journal of TRIZ in Engineering Design 2, 40–58.Google Scholar
Gelle, E., Faltings, B., Clément, D.E., & Smith, I.F.C. (2000). Constraint satisfaction methods for applications in engineering. Engineering With Computers (London) 16(2), 81–95.Google Scholar
Gero, J.S. (1990). Design prototypes: a knowledge representation schema for design. AI Magazine 11(4), 26–36.Google Scholar
Girard, P., & Doumeingts, G. (2004). Modelling the engineering design system to improve performance. Computers and Industrial Engineering 46(1), 43–67.CrossRefGoogle Scholar
Goel, A.K., & Craw, S. (2006). Design, innovation and case-based reasoning. Knowledge Engineering Review 20(3), 271–276.Google Scholar
Gomez De Silva Garza, A., & Maher, M. (1996). Design by interactive exploration using memory-based techniques. Knowledge-Based Systems 9(3), 151–161.Google Scholar
Gu, D.-X., Liang, C.-Y., Bichindaritz, I., Zuo, C.-R., & Wang, J. (2012). A case-based knowledge system for safety evaluation decision making of thermal power plants. Knowledge-Based Systems 26, 185–195.Google Scholar
Guo, Y., Hu, J., & Hong Peng, Y. (2012). A CBR system for injection mould design based on ontology: a case study. Computer-Aided Design 44(6), 496–508.Google Scholar
Haskins, C. (2011). Systems Engineering Handbook: A Guide for Systems Life Cycle Processes and Activities. San Diego, CA: INCOSE.Google Scholar
Huang, C.-C., & Kusiak, A. (1998). Modularity in design of products and systems. IEEE Transactions on Systems, Man, and Cybernetics, Part A 28(1), 66–77.Google Scholar
Huysentruyt, J., & Chen, D. (2010). Contribution to the development of a general theory of design. 8th Int. Conf. Modeling and Simulation, MOSIM 2010, Hammamet, Tunisia.Google Scholar
ISO. (2008). ISO/IEC 15288:2008. Systems and Software Engineering System Life Cycle Processes. Geneva: Author.Google Scholar
Jabrouni, H., Foguem, B.K., Geneste, L., & Vaysse, C. (2011). Continuous improvement through knowledge-guided analysis in experience feedback. Engineering Applications of Artificial Intelligence 24(8), 1419–1431.Google Scholar
Jabrouni, H., Kamsu-Foguem, B., & Geneste, L. (2009). Exploitation of knowledge extracted from industrial feedback processes. Proc. Software, Knowledge and Information Management and Applications, SKIMA 2009, Fes, Morocco.Google Scholar
Janthong, N., Brissaud, D., & Butdee, S. (2010). Combining axiomatic design and case-based reasoning in an innovative design methodology of mechatronics products. CIRP Journal of Manufacturing Science and Technology 2(4), 226–239.Google Scholar
Junker, U., & Mailharro, D. (2003). Preference programming: advanced problem solving for configuration. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 17(1), 13–29.Google Scholar
Kam, C., & Fischer, M. (2004). Capitalizing on early project decision-making opportunities to improve facility design, construction, and life-cycle performance-POP, PM4D, and decision dashboard approaches. Automation in Construction 13(1), 53–65.CrossRefGoogle Scholar
Kim, K.-Y., Manley, D.G., & Yang, H. (2006). Ontology-based assembly design and information sharing for collaborative product development. Computer-Aided Design 38(12), 1233–1250.Google Scholar
Kolb, D.A. (1984). Experiential learning: experience as the source of learning and development. Journal of Organizational Behavior 8, 359–360.Google Scholar
Kolodner, J. (1993). Case-Based Reasoning. San Mateo, CA: Morgan Kaufmann.Google Scholar
Lau, A.S.M., Tsui, E., & Lee, W.B. (2009). An ontology-based similarity measurement for problem-based case reasoning. Expert Systems With Applications 36(3), 6574–6579.Google Scholar
Leake, D., & McSherry, D. (2005). Introduction to the special issue on explanation in case-based reasoning. Artificial Intelligence Review 24(2), 103–108.Google Scholar
Lee, K., & Luo, C. (2002). Application of case-based reasoning in die-casting die design. International Journal of Advanced Manufacturing Technology 20, 284–295.Google Scholar
Liu, D.-R., & Ke, C.-K. (2007). Knowledge support for problem-solving in a production process: a hybrid of knowledge discovery and case-based reasoning. Expert Systems With Applications 33(1), 147–161.Google Scholar
Liu, H.-W. (2005). New similarity measures between intuitionistic fuzzy sets and between elements. Mathematical and Computer Modelling 42(12), 61–70.Google Scholar
Macedo, L., & Cardoso, A. (1998). Nested graph-structured representations for cases. Proc. 4th European Workshop on Advances in Case-Based Reasoning (EWCBR-98) (Smyth, B., & Cunningham, P. Eds.), LNAI, Vol. 1488, pp. 1–12. Berlin: Springer.Google Scholar
Maher, M.-L., & Gomez de Silva Garza, A. (1997). Case-based reasoning in design. IEEE Expert 12(2), 34–41.Google Scholar
Martin, J.N. (2000). Processes for engineering a system: an overview of the ansi/eia 632 standard and its heritage. Systems Engineering 3(1), 1–26.Google Scholar
Mileman, T., Knight, B., Petridis, M., Cowell, D., & Ewer, J. (2002). Case-based retrieval of 3-dimensional shapes for the design of metal castings. Journal of Intelligent Manufacturing 13, 39–45.Google Scholar
Mok, C., Hua, M., & Wong, S. (2008). A hybrid case-based reasoning CAD system for injection mould design. International Journal of Production Research 46(14), 3783–3800.Google Scholar
Mondragon, C.C., Mondragon, A.C., Miller, R., & Mondragon, E C. (2009). Managing technology for highly complex critical modular systems: the case of automotive by-wire systems. International Journal of Production Economics 118(2), 473–485.Google Scholar
Montanari, U. (1974). Networks of constraints: fundamental properties and application to picture processing. Information Science 7, 95–132.Google Scholar
Nanda, J., Thevenot, H.J., Simpson, T.W., Stone, R.B., Bohm, M., & Shooter, S.B. (2007). Product family design knowledge representation, aggregation, reuse, and analysis. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 21(2), 173–192.Google Scholar
Negny, S., & Le-Lann, J. (2008). Case-based reasoning for chemical engineering design. Chemical Engineering Research and Design 86(6), 648–658.Google Scholar
Negny, S., Riesco, H., & Lann, J.-M.L. (2010). Effective retrieval and new indexing method for case based reasoning: application in chemical process design. Engineering Applications of Artificial Intelligence 23(6), 880–894.Google Scholar
Pahl, G., & Beitz, W. (1984). Engineering Design: A Systematic Approach. Berlin: Springer.Google Scholar
Policastro, C.A., de Carvalho, A.C.P.L.F., & Delbem, A.C.B. (2006). Automatic knowledge learning and case adaptation with a hybrid committee approach. Journal of Applied Logic 4(1), 26–38.Google Scholar
Policastro, C.A., de Carvalho, A.C.P.L.F., Delbem, A.C.B. (2008). A hybrid case adaptation approach for case-based reasoning. Applied Intelligence 28(2), 101–119.Google Scholar
Qin, X., & Regli, W. (2003). A study in applying case-based reasoning to engineering design: mechanical bearing design. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 17(3), 235–252.Google Scholar
Rakoto, H., Hermosillo-Worley, J., & Ruet, M. (2002). Integration of experience based decision support in industrial processes. IEEE Int. Conf. Systems, Man and Cybernetics, SMC'02. Hammamet, Tunisia.Google Scholar
Richards, D., & Simoff, S.J. (2001). Design ontology in context—a situated cognition approach to conceptual modelling. Artificial Intelligence in Engineering 15(2), 121–136.Google Scholar
Ruet, M., & Geneste, L. (2002). Search and adaptation in a fuzzy object oriented case base. Proc. 6th European Conf. Case Based Reasoning, LNAI, Vol. 2416, pp. 350–364. Berlin: Springer.Google Scholar
Saridakis, K., & Dentsoras, A. (2007). Case-desc: a system for case-based design with soft computing techniques. Expert Systems With Applications 32(2), 641–657.Google Scholar
Settouti, L.S., Prié, Y., Marty, J.-C., & Mille, A. (2009). A trace-based system for technology-enhanced learning systems personalisation. Proc. 9th IEEE Int. Conf. Advance Learning Technologies, pp. 93–97.Google Scholar
Simon, H. (1969). The Sciences of the Artificial. Cambridge, MA: MIT Press.Google Scholar
Stahl, A., & Bergmann, R. (2000). Applying recursive CBR for the customization of structured products in an electronic shop. Advances in Case-Based Reasoning (Blanzieri, E., & Portinale, L. Eds.), LNCS, Vol. 1898, pp. 297–308. Berlin: Springer.Google Scholar
Studer, R., Benjamins, V.R., & Fensel, D. (1998). Knowledge engineering: principles and methods. Data & Knowledge Engineering 25(1–2), 161–197.Google Scholar
Suh, N.P. (1990). The Principles of Design. New York: Oxford University Press.Google Scholar
Sun, Z., Han, J., & Dong, D. (2008). Five perspectives on case based reasoning. Advanced Intelligent Computing Theories and Applications: With Aspects of Artificial Intelligence (Huang, D.-S., Wunsch, D.C., Levine, D., & Jo, K.-H., Eds.), LNSC, Vol. 5227, pp. 410–419. Berlin: Springer.Google Scholar
Tang, M. (1997). A knowledge-based architecture for intelligent design support. International Journal of Knowledge Engineering Review 12(4), 387–460.Google Scholar
Thornton, A.C. (1996). The use of constraint-based design knowledge to improve the search for feasible designs. Engineering Applications of Artificial Intelligence 9(4), 393–402.Google Scholar
Ullman, D. (2003). The Mechanical Design Process. New York: McGraw–Hill Higher Education.Google Scholar
Uschold, M., & Gruninger, M. (1996). Ontologies: principles, methods and applications. Knowledge Sharing and Review 11(2), 93–155.Google Scholar
Vareilles, E., Aldanondo, M., de Boisse, A.C., Coudert, T., Gaborit, P., & Geneste, L. (2012). How to take into account general and contextual knowledge for interactive aiding design: towards the coupling of csp and cbr approaches. Engineering Applications of Artificial Intelligence 25(1), 31–47.Google Scholar
Wang, J., Tang, M., & Gabrys, B. (2006). An agent-based system supporting collaborative product design. Knowledge-Based Intelligent Information and Engineering Systems (Heidelberg, S.-V.B., Ed.), LNAI, Vol. 4252, Part II, pp. 670–677. Berlin: Springer.CrossRefGoogle Scholar
Wang, W.-J. (1997). New similarity measures on fuzzy sets and on elements. Fuzzy Sets and Systems 85(3), 305–309.Google Scholar
Weber, R., Aha, D.W., & Becerra-Fernandez, I. (2001). Intelligent lessons learned systems. Expert System Applications 20(1), 17–34.Google Scholar
Woon, F.L., Knight, B., Petridis, M., & Patel, M.K. (2005). CBE-conveyor: a case-based reasoning system to assist engineers in designing conveyor systems. Case-Based Reasoning Research and Development (Muñoz-Avila, H., & Ricci, F., Eds.), LNCS, Vol. 3620, pp. 640–651. Berlin: Springer.Google Scholar
Wu, M.-C., Lo, Y.-F., & Hsu, S.-H. (2008). A fuzzy cbr technique for generating product ideas. Expert Systems With Applications 34(1), 530–540.Google Scholar
Wu, Z., & Palmer, M. (1994). Verb semantics and lexical selection. Proc. 32nd Annual Meeting of the Association for Computational Linguistics, pp. 133–138, New Mexico State University, Las Cruces.Google Scholar
Xuanyuan, S., Jiang, Z., Li, Y., & Li, Z. (2011). Case reuse based product fuzzy configuration. Advanced Engineering Informatics 25(2), 193–197.Google Scholar
Yang, C., & Chen, J. (2011). Accelerating preliminary eco-innovation design for products that integrates case-based reasoning and TRIZ method. Journal of Cleaner Production 19, 998–1006.Google Scholar
Zarandi, M.F., Razaee, Z.S., & Karbasian, M. (2011). A fuzzy case based reasoning approach to value engineering. Expert Systems With Applications 38(8), 9334–9339.Google Scholar