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A scalable approach for ideation in biologically inspired design
| Content Provider | Scilit |
|---|---|
| Author | Vandevenne, Dennis Verhaegen, Paul-Armand Dewulf, Simon Duflou, Joost R. |
| Copyright Year | 2014 |
| Description | This paper presents a bioinspiration approach that is able to scalably leverage the ever-growing body of biological information in natural-language format. The ideation tool AskNature, developed by the Biomimicry 3.8 Institute, is expanded with an algorithm for automated classification of biological strategies into the Biomimicry Taxonomy, a three-level, hierarchical information structure that organizes AskNature's database. In this way, the manual work entailed by the classification of biological strategies can be alleviated. Thus, the bottleneck is removed that currently prevents the integration of large numbers of biological strategies. To demonstrate the feasibility of building a scalable bioideation system, this paper presents tests that classify biological strategies from AskNature's reference database for those Biomimicry Taxonomy classes that currently hold sufficient reference documents. |
| Related Links | https://www.cambridge.org/core/services/aop-cambridge-core/content/view/C0918E86E9DB70DC728989F2A9CDE63F/S0890060414000122a.pdf/div-class-title-a-scalable-approach-for-ideation-in-biologically-inspired-design-div.pdf |
| Ending Page | 31 |
| Page Count | 13 |
| Starting Page | 19 |
| ISSN | 08900604 |
| e-ISSN | 14691760 |
| DOI | 10.1017/s0890060414000122 |
| Journal | Artificial Intelligence for Engineering Design, Analysis and Manufacturing |
| Issue Number | 1 |
| Volume Number | 29 |
| Language | English |
| Publisher | Cambridge University Press (CUP) |
| Publisher Date | 2015-02-01 |
| Access Restriction | Open |
| Subject Keyword | Artificial Intelligence for Engineering Design, Analysis and Manufacturing Software Engineering Mathematical and Computational Biology Bioinspired Design Creativity and Ideation |
| Content Type | Text |
| Resource Type | Article |
| Subject | Industrial and Manufacturing Engineering Artificial Intelligence |