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Title: Causal Neurosymbolic AI: A Synergy Between Causality and Neurosymbolic Methods
Award ID(s):
2335967
PAR ID:
10530774
Author(s) / Creator(s):
; ;
Publisher / Repository:
IEEE
Date Published:
Journal Name:
IEEE Intelligent Systems
Volume:
39
Issue:
3
ISSN:
1541-1672
Page Range / eLocation ID:
13 to 19
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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  1. 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. 
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