{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T03:07:21Z","timestamp":1761620841200},"reference-count":23,"publisher":"Cambridge University Press (CUP)","issue":"1","license":[{"start":{"date-parts":[[2003,8,7]],"date-time":"2003-08-07T00:00:00Z","timestamp":1060214400000},"content-version":"unspecified","delay-in-days":187,"URL":"https:\/\/www.cambridge.org\/core\/terms"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AIEDAM"],"published-print":{"date-parts":[[2003,2]]},"abstract":"<jats:p>Configuration problems often involve large product catalogs, and the \ngiven user requests can be met by many different kinds of parts from \nthis catalog. Hence, configuration problems are often weakly \nconstrained and have many solutions. However, many of those solutions \nmay be discarded by the user as long as more interesting solutions are \npossible. The user often prefers certain choices to others (e.g., a red \ncolor for a car to a blue color) or prefers solutions that minimize or \nmaximize certain criteria such as price and quality. In order to \nprovide satisfactory solutions, a configurator needs to address user \npreferences and user wishes. Another important problem is to provide \nhigh-level features to control different reasoning tasks such as \nsolution search, explanation, consistency checking, and \nreconfiguration. We address those problems by introducing a preference \nprogramming system that provides a new paradigm for expressing user \npreferences and user wishes and provides search strategies in a \ndeclarative and unified way, such that they can be embedded in a \nconstraint and rule language. The preference programming approach is \ncompletely open and dynamic. In fact, preferences can be assembled from \ndifferent sources such as business rules, databases, annotations of the \nobject model, or user input. An advanced topic is to elicit preferences \nfrom user interactions, especially from explanations of why a user \nrejects proposed choices. Our preference programming system has \nsuccessfully been used in different configuration domains such as loan \nconfiguration, service configuration, and other problems.<\/jats:p>","DOI":"10.1017\/s089006040317103x","type":"journal-article","created":{"date-parts":[[2003,10,16]],"date-time":"2003-10-16T08:39:57Z","timestamp":1066293597000},"page":"13-29","source":"Crossref","is-referenced-by-count":17,"title":["Preference programming: Advanced problem solving for configuration"],"prefix":"10.1017","volume":"17","author":[{"given":"ULRICH","family":"JUNKER","sequence":"first","affiliation":[]},{"given":"DANIEL","family":"MAILHARRO","sequence":"additional","affiliation":[]}],"member":"56","published-online":{"date-parts":[[2003,8,7]]},"reference":[{"key":"S089006040317103X_ref010","unstructured":"Haselb\u00f6ck, A. & Stumptner, M. (1993).An integrated approach for modelling complex configurationdomains.13th Int. Conf. Expert Systems, AI, and Natural Language,Avignon, France, pp.625\u2013634."},{"key":"S089006040317103X_ref005","doi-asserted-by":"crossref","unstructured":"Delgrande, J.P. & Schaub, T. (2000).Expressing preferences in default logic.Artificial Intelligence 123(1\u20132),41\u201387.","DOI":"10.1016\/S0004-3702(00)00049-7"},{"key":"S089006040317103X_ref019","unstructured":"Puget, J.-F. (1992).Object-oriented constraint programming.Artificial Intelligence, Expert Systems, Natural Language: TwelfthInt. Conf., pp.129\u2013138,Avignon, France."},{"key":"S089006040317103X_ref021","unstructured":"Soininen, T. , Niemel\u00e4, I. , Tiihonen, J. , & Sulonen, R. (2000).Unified configuration knowledge representation using weightconstraint rules.ECAI-2000 Workshop on Configuration,Berlin, pp.79\u201384."},{"key":"S089006040317103X_ref006","unstructured":"Domshlak, C. , Brafman, R.I. , & Shimony, S.E. (2001).Preference-based configuration of web page content.Proc. Seventeenth Int. Joint Conf. Artificial Intelligence, pp.1451\u20131456.San Francisco, CA:Morgan Kaufmann."},{"key":"S089006040317103X_ref016","doi-asserted-by":"crossref","unstructured":"Mailharro, D. (1998).A classification and constraint based framework for configuration.Artificial Intelligence for Engineering Design, Analysis andManufacturing 12(4),383\u2013397.","DOI":"10.1017\/S0890060498124101"},{"key":"S089006040317103X_ref007","unstructured":"Doyle, J. (2002).Preferences: Some problems and prospects. InAAAI-02 Workshop on Preferences in AI and CP: Symbolic Approaches.Menlo Park:AAAI Press."},{"key":"S089006040317103X_ref023","unstructured":"van Hentenryck, P. & Puget, J.-F. (2000).Search and strategies in OPL.ACMTCL: ACM Transactions on Computational Logic 1."},{"key":"S089006040317103X_ref020","unstructured":"SABRE .(2002).Sabre trip shopping: Product information.Available on-line atwww.sabretravelnetwork.com\/products_and_services\/travel_agencies\/s1_000999.htm."},{"key":"S089006040317103X_ref003","unstructured":"Brewka, G. (1989).Preferred subtheories: An extended logical theory for defaultreasoning.Proc. Eleventh Int. Joint Conf. Artificial Intelligence( Sridharan, N.S. , Ed.), pp.1043\u20131048.San Mateo, CA:Morgan Kaufmann."},{"key":"S089006040317103X_ref008","unstructured":"Ehrgott, M. (1997).A characterization of lexicographic max-ordering solutions.Methods of Multicriteria Decision Theory: Proceedings of the 6thWorkshop of the DGOR Working-Group Multicriteria Optimization andDecision Theory, pp.193\u2013202.Egelsbach, Germany:H\u00e4sel-Hohenhausen."},{"key":"S089006040317103X_ref001","doi-asserted-by":"crossref","unstructured":"Bistarelli, S. , Montanari, U. , Rossi, F. , Schiex, T. , Verfaillie, G. , & Fargier, H. (1999).Semiring-based CSPs and valued CSPs: Frameworks, properties, andcomparison.Constraints 4(3),199\u2013240.","DOI":"10.1023\/A:1026441215081"},{"key":"S089006040317103X_ref013","unstructured":"Junker, U. (1997).A cumulative-model semantics for dynamic preferences onassumptions.Proc. Fifteenth Int. Joint Conf. Artificial Intelligence, pp.162\u2013167.San Francisco, CA:Morgan Kaufmann."},{"key":"S089006040317103X_ref022","unstructured":"Torrens, M. & Faltings, B. (2002).Using soft CSPs for approximating Pareto-optimal solutions sets.AAAI-02 Workshop on Preferences in AI and CP: Symbolic Approaches.Menlo Park, CA:AAAI Press."},{"key":"S089006040317103X_ref004","unstructured":"Brewka, G. (1994).Reasoning about priorities in default logic.Proc. Twelfth National Conf. Artificial Intelligence (AAAI), pp.940\u2013945.Menlo Park, CA:AAAI Press."},{"key":"S089006040317103X_ref009","unstructured":"Freuder, E.C. (1991).Eliminating interchangeable values in constraint satisfactionproblems.Proc. Ninth National Conf. Artificial Intelligence (AAAI)( Dean, K. & McKeown, T.L. , Eds.), pp.227\u2013233.Cambridge, MA:MIT Press."},{"key":"S089006040317103X_ref011","unstructured":"ILOG .(2002).ILOG JConfigurator V2.0: Product information.Available on-line atwww.ilog.com\/products\/jconfigurator\/."},{"key":"S089006040317103X_ref018","doi-asserted-by":"crossref","unstructured":"Poole, D. 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InActes des Vi\u00e8mes Journ\u00e9es du Laboratoired'Informatique de Paris Nord( Bidoit, N. , Ed.), pp.1\u201321.Paris:Villetaneuse."},{"key":"S089006040317103X_ref014","unstructured":"Junker, U. (2000).Preference-based search for scheduling.Proc. Seventeenth National Conf. Artificial Intelligence (AAAI), pp.904\u2013909.Menlo Park, CA:AAAI Press."},{"key":"S089006040317103X_ref017","unstructured":"Mittal, S. & Frayman, F. (1989).Towards a generic model of configuration tasks.Proc. Eleventh Int. Joint Conf. 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