Neurosymbolic Programming

By Swarat Chaudhuri, Kevin Ellis, Oleksandr Polozov, Rishabh Singh, Armando Solar-Lezama, Yisong Yue. 2021

113 citations recorded by the source corpus.

We survey recent work on neurosymbolic programming, an emerging area that bridges the areas of deep learning and program synthesis. Like in classic machine learning, the goal here is to learn functions from data. However, these functions are represented as programs that can use neural modules in addition to symbolic primitives and are induced using a combination of symbolic search and gradient-based optimization. Neurosymbolic programming can offer multiple advantages over end-to-end deep learning. Programs can sometimes naturally represent long-horizon, procedural tasks that are difficult to perform using deep networks. Neurosymbolic representations are also, commonly, easier to interpret and formally verify than neural networks. The restrictions of a programming language can serve as a form of regularization and lead to more generalizable and data-efficient Swarat Chaudhuri, Kevin Ellis, Oleksandr Polozov, Rishabh Singh, Armando SolarLezama and Yisong Yue (2021), “Neurosymbolic Programming”, Foundations and Trends® in Programming Languages: Vol. 7, No. 3, pp 158–243. DOI: 10.1561/2500000049. ©2021 S. Chaudhuri et al. The version of record is available at: http://dx.doi.org/10.1561/2500000049

Subfields: Artificial Intelligence, Cognitive Neuroscience, Knowledge Graphs & Representation

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Citations in the corpus

MultiPL-E: A Scalable and Polyglot Approach to Benchmarking Neural Code Generation (2023, background): “Code generation has long been a task of interest: there is extensive work on program synthesis [11], [12], [13] using both symbolic and neuro-symbolic approaches.”
A Hazard Analysis Framework for Code Synthesis Large Language Models (2022, neutral): “Deriving from programming language synthesis techniques may lead the way on how this can be carried out [9, 12, 16, 18, 32].”
A Scalable and Extensible Approach to Benchmarking NL2Code for 18 Programming Languages (2022, background): “Code generation has long been a task of interest: there is extensive work on program synthesis (Alur et al. 2013; Chaudhuri et al. 2021) using both symbolic and neuro-symbolic approaches.”
Neurosymbolic Program Synthesis (unknown): “Neurosymbolic machine learning [1,2,3] is an increasingly prominent response to these concerns.”
Neurosymbolic AI: the 3rd wave (2020, background): “Program synthesis based more generally on probabilistic languages other than logic have also flourished as part of the research area known as neurosymbolic programming (Chaudhuri et al. 2021).”
Can Transformers Learn to Solve Problems Recursively? (2023, methodology): “Program and Proof Synthesis The tasks we choose are inspired by work in program and proof synthesis [15, 19, 29, 26, 34, 33, 23, 3], and are also an important class of functions for inductive logic programming [8].”
MultiPL-E: A Scalable and Extensible Approach to Benchmarking Neural Code Generation (2022, background): “Code generation has long been a task of interest: there is extensive work on program synthesis [12, 13, 14] using both symbolic and neuro-symbolic approaches.”