AI Challenges in Synthetic Biology Engineering

Authors

  • Fusun Yaman BBN Technologies
  • Aaron Adler BBN Technologies
  • Jacob Beal BBN Technologies

DOI:

https://doi.org/10.1609/aaai.v32i1.11315

Keywords:

Synthetic Biology, Challenges, knowledge-based systems, knowledge representation, semantic networks, frame representations, machine learning, hypothesis generation, expert systems, constraint-based reasoning, planning under uncertainty, robotics

Abstract

A wide variety of Artificial Intelligence (AI) techniques, from expert systems to machine learning to robotics, are needed in the field of synthetic biology. This paper describes the design-build-test engineering cycle and lists some challenges in which AI can help.

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Published

2018-04-27

How to Cite

Yaman, F., Adler, A., & Beal, J. (2018). AI Challenges in Synthetic Biology Engineering. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). https://doi.org/10.1609/aaai.v32i1.11315

Issue

Vol. 32 No. 1 (2018): Thirty-Second AAAI Conference on Artificial Intelligence

Section

IAAI Technical: Challenge Papers