Scallop: A Language for Neurosymbolic Programming

We present Scallop, a language which combines the benefits of deep learning and logical reasoning. Scallop enables users to write a wide range of neurosymbolic applications and train them in a data- and compute-efficient manner. It achieves these goals through three key features: 1) a flexible symbo...

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Bibliographic Details
Published in:Proceedings of ACM on programming languages Vol. 7; no. PLDI; pp. 1463 - 1487
Main Authors: Li, Ziyang, Huang, Jiani, Naik, Mayur
Format: Journal Article
Language:English
Published: New York, NY, USA ACM 06.06.2023
Subjects:
Computing methodologies
Domain specific languages
Learning paradigms
Logical and relational learning
Probabilistic reasoning
Software and its engineering
Differentiable reasoning
Neurosymbolic methods
ISSN:2475-1421, 2475-1421
Online Access:Get full text
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Summary:We present Scallop, a language which combines the benefits of deep learning and logical reasoning. Scallop enables users to write a wide range of neurosymbolic applications and train them in a data- and compute-efficient manner. It achieves these goals through three key features: 1) a flexible symbolic representation that is based on the relational data model; 2) a declarative logic programming language that is based on Datalog and supports recursion, aggregation, and negation; and 3) a framework for automatic and efficient differentiable reasoning that is based on the theory of provenance semirings. We evaluate Scallop on a suite of eight neurosymbolic applications from the literature. Our evaluation demonstrates that Scallop is capable of expressing algorithmic reasoning in diverse and challenging AI tasks, provides a succinct interface for machine learning programmers to integrate logical domain knowledge, and yields solutions that are comparable or superior to state-of-the-art models in terms of accuracy. Furthermore, Scallop's solutions outperform these models in aspects such as runtime and data efficiency, interpretability, and generalizability.
ISSN:2475-1421
2475-1421
DOI:10.1145/3591280