Neurosymbolic Programming for Science
Name
view6.pdf
Description
Main Article
Size
862.77 KB
Format
Adobe PDF
Checksum (MD5)
ddedf679cf61af9816a5f7565569fb3f
Author(s)
Sun, Jennifer J
Tjandrasuwita, Megan
Sehgal, Atharva
Solar-Lezama, Armando
Chaudhuri, Swarat
Yue, Yisong
Costilla Reyes, Omar
Date Issued
October 12, 2022
Abstract
Neurosymbolic Programming (NP) techniques have the potential to accelerate scientific discovery across fields. These models combine neural and symbolic components to learn complex patterns and representations from data, using high-level concepts or known constraints. As a result, NP techniques can interface with symbolic domain knowledge from scientists, such as prior knowledge and experimental context, to produce interpretable outputs. Here, we identify opportunities and challenges between current NP models and scientific workflows, with real-world examples from behavior analysis in science. We define concrete next steps to move the NP for science field forward, to enable its use broadly for workflows across the natural and social sciences.
Subjects
programming languages
deep learning
science
domain knowledge
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