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Computer Science > Artificial Intelligence

arXiv:2210.05050 (cs)
[Submitted on 10 Oct 2022 (v1), last revised 7 Nov 2022 (this
version, v2)]

Title:Neurosymbolic Programming for Science

Authors:Jennifer J. Sun, Megan Tjandrasuwita, Atharva Sehgal, Armando
Solar-Lezama, Swarat Chaudhuri, Yisong Yue, Omar Costilla-Reyes
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    Abstract: Neurosymbolic Programming (NP) techniques have the
    potential to accelerate scientific discovery. These models
    combine neural and symbolic components to learn complex patterns
    and representations from data, using high-level concepts or known
    constraints. NP techniques can interface with symbolic domain
    knowledge from scientists, such as prior knowledge and
    experimental context, to produce interpretable outputs. We
    identify opportunities and challenges between current NP models
    and scientific workflows, with real-world examples from behavior
    analysis in science: to enable the use of NP broadly for
    workflows across the natural and social sciences.

Comments: Neural Information Processing Systems 2022 - AI for science
          workshop
Subjects: Artificial Intelligence (cs.AI)
Cite as:  arXiv:2210.05050 [cs.AI]
          (or arXiv:2210.05050v2 [cs.AI] for this version)
          https://doi.org/10.48550/arXiv.2210.05050
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Omar Costilla Reyes [view email]
[v1] Mon, 10 Oct 2022 23:46:41 UTC (630 KB)
[v2] Mon, 7 Nov 2022 15:21:32 UTC (1,751 KB)
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