Imitation of Life: A Search Engine for Biologically Inspired Design

Authors

  • Hen Emuna The Hebrew University of Jerusalem
  • Nadav Borenstein University of Copenhagen
  • Xin Qian University of Maryland
  • Hyeonsu Kang Carnegie Mellon University
  • Joel Chan University of Maryland
  • Aniket Kittur Carnegie Mellon University
  • Dafna Shahaf The Hebrew University of Jerusalem

DOI:

https://doi.org/10.1609/aaai.v38i1.27805

Keywords:

CMS: Analogy, APP: Other Applications

Abstract

Biologically Inspired Design (BID), or Biomimicry, is a problem-solving methodology that applies analogies from nature to solve engineering challenges. For example, Speedo engineers designed swimsuits based on shark skin. Finding relevant biological solutions for real-world problems poses significant challenges, both due to the limited biological knowledge engineers and designers typically possess and to the limited BID resources. Existing BID datasets are hand-curated and small, and scaling them up requires costly human annotations. In this paper, we introduce BARcode (Biological Analogy Retriever), a search engine for automatically mining bio-inspirations from the web at scale. Using advances in natural language understanding and data programming, BARcode identifies potential inspirations for engineering challenges. Our experiments demonstrate that BARcode can retrieve inspirations that are valuable to engineers and designers tackling real-world problems, as well as recover famous historical BID examples. We release data and code; we view BARcode as a step towards addressing the challenges that have historically hindered the practical application of BID to engineering innovation.

Published

2024-03-25

How to Cite

Emuna, H., Borenstein, N., Qian, X., Kang, H., Chan, J., Kittur, A. ., & Shahaf, D. (2024). Imitation of Life: A Search Engine for Biologically Inspired Design. Proceedings of the AAAI Conference on Artificial Intelligence, 38(1), 503-511. https://doi.org/10.1609/aaai.v38i1.27805

Issue

Section

AAAI Technical Track on Cognitive Modeling & Cognitive Systems