{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,12]],"date-time":"2025-04-12T09:26:27Z","timestamp":1744449987172},"reference-count":10,"publisher":"Cambridge University Press (CUP)","issue":"2","license":[{"start":{"date-parts":[[2009,2,27]],"date-time":"2009-02-27T00:00:00Z","timestamp":1235692800000},"content-version":"unspecified","delay-in-days":4715,"URL":"https:\/\/www.cambridge.org\/core\/terms"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AIEDAM"],"published-print":{"date-parts":[[1996,4]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Analogical reasoning plays an important role in design. In particular, cross-domain analogies appear to be important in innovative and creative design. However, making cross-domain analogies is hard and often requires abstractions common to the source and target domains. Recent work in case-based design suggests that generic mechanisms are one type of abstractions useful in adapting past designs. However, one important yet unexplored issue is where these generic mechanisms come from. We hypothesize that they are acquired incrementally from design experiences in familiar domains by abstraction over patterns of regularity. Three important issues in abstraction from experiences are what to abstract from an experience, how far to abstract, and what methods to use. In this short paper, we describe how structure-behavior-function models of designs in a familiar domain provide the content, and together with the problem-solving context in which learning occurs, also provide the constraints for learning generic mechanisms from design experiences. In particular, we describe the model-based learning method with a scenario of learning feedback mechanism.<\/jats:p>","DOI":"10.1017\/s0890060400001372","type":"journal-article","created":{"date-parts":[[2010,3,31]],"date-time":"2010-03-31T09:45:57Z","timestamp":1270028757000},"page":"131-136","source":"Crossref","is-referenced-by-count":24,"title":["From design experiences to generic mechanisms: Model-based learning in analogical design"],"prefix":"10.1017","volume":"10","author":[{"given":"Sambasiva R.","family":"Bhatta","sequence":"first","affiliation":[]},{"given":"Ashok K.","family":"Goel","sequence":"additional","affiliation":[]}],"member":"56","published-online":{"date-parts":[[2009,2,27]]},"reference":[{"key":"S0890060400001372_ref005","doi-asserted-by":"publisher","DOI":"10.1109\/2.179157"},{"key":"S0890060400001372_ref007","doi-asserted-by":"publisher","DOI":"10.1016\/0004-3702(83)90016-4"},{"key":"S0890060400001372_ref006","unstructured":"Goel A. (1989). Integration of case-based reasoning and model-based reasoning for adaptive design problem solving. Ph.D. diss., Dept. of Comp. and Info. Sci., The Ohio State University."},{"key":"S0890060400001372_ref002","unstructured":"Bhatta S. , & Goel A. (1994a). From design experiences to generic mechanisms: Model-based learning in analogical design. Proc. AID'94 Workshop Machine Learning in Design."},{"key":"S0890060400001372_ref010","unstructured":"Stroulia E. , & Goel A. (1992). Generic teleological mechanisms and their use in case adaptation. Proc. Fourteenth Ann. Conf. Cog. Sci. Soc., 319\u2013324."},{"key":"S0890060400001372_ref001","unstructured":"Bhatta S. , & Goel A. (1993). Learning generic mechanisms from experiences for analogical reasoning. Proc. of the Fifteenth Annual Conf. Cog. Sci. Soc., 237\u2013242."},{"key":"S0890060400001372_ref003","first-page":"113","article-title":"Discovery of physical principles from design experiences","volume":"8","author":"Bhatta","year":"1994","journal-title":"AI EDAM"},{"key":"S0890060400001372_ref004","doi-asserted-by":"publisher","DOI":"10.1016\/S0020-7373(05)80088-4"},{"key":"S0890060400001372_ref008","volume-title":"Microelectronic circuits","author":"Sedra","year":"1991"},{"key":"S0890060400001372_ref009","first-page":"47","volume-title":"Experience, Memory and Reasoning","author":"Sembugamoorthy","year":"1986"}],"container-title":["Artificial Intelligence for Engineering Design, Analysis and Manufacturing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.cambridge.org\/core\/services\/aop-cambridge-core\/content\/view\/S0890060400001372","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,5,12]],"date-time":"2019-05-12T17:53:49Z","timestamp":1557683629000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.cambridge.org\/core\/product\/identifier\/S0890060400001372\/type\/journal_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[1996,4]]},"references-count":10,"journal-issue":{"issue":"2","published-print":{"date-parts":[[1996,4]]}},"alternative-id":["S0890060400001372"],"URL":"https:\/\/doi.org\/10.1017\/s0890060400001372","relation":{},"ISSN":["0890-0604","1469-1760"],"issn-type":[{"value":"0890-0604","type":"print"},{"value":"1469-1760","type":"electronic"}],"subject":[],"published":{"date-parts":[[1996,4]]}}}