{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,15]],"date-time":"2024-09-15T18:41:50Z","timestamp":1726425710499},"reference-count":12,"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:p>According to Simon (1983), machine learning is a process that improves the performance of an intelligent system. The performance task of a design system is to arrive at a structural description of a device given its functional description. Machine learning can occur, to improve the performance of the design system, in a number of ways. The learning can make the process of arriving at a known structure to function mapping faster, either by improving the control strategy or by learning new world knowledge needed for the structure to function mapping. It can also improve the performance of the design system by allowing it to come up with new structure to function mappings. The latter kinds of learning leads to innovative designs. The work presented in this paper, EnviroAdapt, belongs to the latter category. EnviroAdapt has the performance task of designing devices along with their operating environments. The EnviroAdapt learns about the novel environments and designs devices for these environments. This learning process is enabled by adapting the previous designs of devices along with their operating environments. This adaptation process makes use of abstracting general mechanisms from the previous designs, then instantiating them in the context of new design problem. Both of these processes are driven by the characteristics of the new design problem that requires a device to operate within a novel operating environment.<\/jats:p>","DOI":"10.1017\/s0890060400001438","type":"journal-article","created":{"date-parts":[[2010,3,31]],"date-time":"2010-03-31T09:45:57Z","timestamp":1270028757000},"page":"151-156","source":"Crossref","is-referenced-by-count":2,"title":["Learning about novel operating environments: Designing by adaptive modelling"],"prefix":"10.1017","volume":"10","author":[{"given":"S.","family":"Prabhakar","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"A. K.","family":"Goel","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"56","published-online":{"date-parts":[[2009,2,27]]},"reference":[{"key":"S0890060400001438_ref004","unstructured":"Goel A.K. , & Chandrasekaran B. (1989). Functional representation of designs and redesign problem solving. Proc. Eleventh Int. Joint Conf. Artif. Intell., 1388\u20131394."},{"key":"S0890060400001438_ref005","first-page":"377","volume-title":"Proc. Australian New Zealand Intell. Info. Syst. Conf.","author":"Goel","year":"1994"},{"key":"S0890060400001438_ref006","unstructured":"Prabhakar S. , & Goel A.K. (1992). Performance driven creativity in design: Constraint discovery, model revision, and case composition. Second Int. Round-Table Conf. Computational Models of Creative Design, 101\u2013127."},{"key":"S0890060400001438_ref010","first-page":"25","volume-title":"Machine Learning: An Artificial Intelligence Approach","author":"Simon","year":"1983"},{"key":"S0890060400001438_ref012","doi-asserted-by":"crossref","unstructured":"Stroulia E. , Shankar M. , Goel A. , & Penberthy L. (1992). A model-based approach to blame assignment in design, Technical Report, College of Computing, Georgia Institute of Technology, Atlanta.","DOI":"10.1007\/978-94-011-2787-5_26"},{"key":"S0890060400001438_ref007","unstructured":"Prabhakar S. , Goel A. , & Bhatta S. (1995). Adaptive modelling in the design of interactive devices: Towards a computational theory of engineering invention. Third Int. Round-Table Conf. Computational Models of Creative Design, 267\u2013301."},{"key":"S0890060400001438_ref008","first-page":"47","volume-title":"Experience, Memory and Reasoning","author":"Sembugamoorthy","year":"1986"},{"key":"S0890060400001438_ref001","unstructured":"Bhatta S. , & Goel A. (1993). Learning generic mechanisms from experiences for analogical reasoning. Proc. Fifteenth Annual Conf. Cognitive Sci. 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Soc, 319\u2013324."}],"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\/S0890060400001438","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,5,12]],"date-time":"2019-05-12T17:54:19Z","timestamp":1557683659000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.cambridge.org\/core\/product\/identifier\/S0890060400001438\/type\/journal_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[1996,4]]},"references-count":12,"journal-issue":{"issue":"2","published-print":{"date-parts":[[1996,4]]}},"alternative-id":["S0890060400001438"],"URL":"https:\/\/doi.org\/10.1017\/s0890060400001438","relation":{},"ISSN":["0890-0604","1469-1760"],"issn-type":[{"type":"print","value":"0890-0604"},{"type":"electronic","value":"1469-1760"}],"subject":[],"published":{"date-parts":[[1996,4]]}}}