Academic Journal
New roles for machine learning in design
| Τίτλος: | New roles for machine learning in design |
|---|---|
| Συγγραφείς: | Yoram Reich, Carnegie Mellon University.Engineering Design Research Center. |
| Έτος έκδοσης: | 2018 |
| Συλλογή: | KiltHub Research from Carnegie Mellon University |
| Θεματικοί όροι: | Electrical engineering not elsewhere classified, Digital processor architectures, Other information and computing sciences not elsewhere classified, Machine learning, Engineering design Computer programs, Computer Engineering, Electrical and Electronic Engineering not elsewhere classified |
| Περιγραφή: | "Research on machine learning in design has concentrated on the use and development of techniques that can solve simple well-defined problems. Invariably, this effort, while important at the early stages of the development of the field, cannot scale up to address real design problems since all existing techniques are based on simplifying assumptions that do not hold for real design. In particular they do not address the dependence on context and multiple, often conflicting, interests that are constitutive of design. This paper analyzes the present situation and criticizes a number of prevailing views. Subsequently, the paper offers an alternative approach whose goal is to advance the use of machine learning in design practice. The approach is partially integrated into a modeling system called n-dim. The use of machine learning in n-dim is presented and open research issues are outlined." |
| Τύπος εγγράφου: | article in journal/newspaper |
| Γλώσσα: | unknown |
| Relation: | https://figshare.com/articles/journal_contribution/New_roles_for_machine_learning_in_design/6469076 |
| DOI: | 10.1184/R1/6469076.v1 |
| Διαθεσιμότητα: | https://doi.org/10.1184/R1/6469076.v1 https://figshare.com/articles/journal_contribution/New_roles_for_machine_learning_in_design/6469076 |
| Rights: | In Copyright |
| Αριθμός Καταχώρησης: | edsbas.D02E34B9 |
| Βάση Δεδομένων: | BASE |
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