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Symbolic Computation of an Arbitrary-Order Resonance Condition in a Hamiltonian System
AbstractThe study of formal stability of equilibrium positions of a multiparametric Hamiltonian system in a generic case is traditionally carried out...
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Neuro-symbolic artificial intelligence: a survey
The goal of the growing discipline of neuro-symbolic artificial intelligence (AI) is to develop AI systems with more human-like reasoning...
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Efficient generator of mathematical expressions for symbolic regression
We propose an approach to symbolic regression based on a novel variational autoencoder for generating hierarchical structures, HVAE. It combines...
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Language model-accelerated deep symbolic optimization
Symbolic optimization methods have been used to solve varied challenging and relevant problems such as symbolic regression and neural architecture...
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Interpretable scientific discovery with symbolic regression: a review
Symbolic regression is emerging as a promising machine learning method for learning succinct underlying interpretable mathematical expressions...
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Collaborative Decision Support Systems Based on Neuro-Symbolic Artificial Intelligence: Problems and Generalized Conceptual Model
AbstractThe development of artificial intelligence technologies and the growing complexity of decision-making when managing complex dynamic systems...
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Symbolic-Numerical Implementation of the Galerkin Method for Approximate Solution of the Waveguide Diffraction Problem
AbstractIn this paper, we construct a symbolic-numerical implementation of the Galerkin method for approximate solution of the waveguide diffraction...
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Temporal learner modelling through integration of neural and symbolic architectures
Neural and symbolic architectures are key techniques in AI for learner modelling, enhancing adaptive educational services. Symbolic models offer...
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FFNSL: Feed-Forward Neural-Symbolic Learner
Logic-based machine learning aims to learn general, interpretable knowledge in a data-efficient manner. However, labelled data must be specified in a...
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Symbolic-Numerical Implementation of the Model of Adiabatic Guided Modes for Two-Dimensional Irregular Waveguides
AbstractIn this work, a symbolic-numerical solution of Maxwell’s equations is constructed, describing the guided modes of a two-dimensional smoothly...
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Symbolic knowledge injection meets intelligent agents: QoS metrics and experiments
Bridging intelligent symbolic agents and sub-symbolic predictors is a long-standing research goal in AI. Among the recent integration efforts,...
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Reduced implication-bias logic loss for neuro-symbolic learning
Integrating logical reasoning and machine learning by approximating logical inference with differentiable operators is a widely used technique in the...
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Neuro-Symbolic Reasoning for Multimodal Referring Expression Comprehension in HMI Systems
Conventional Human–Machine Interaction (HMI) interfaces have predominantly relied on GUI and voice commands. However, natural human communication...
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Learning explanatory logical rules in non-linear domains: a neuro-symbolic approach
Deep neural networks, despite their capabilities, are constrained by the need for large-scale training data, and often fall short in generalisation...
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A unit-based symbolic execution method for detecting memory corruption vulnerabilities in executable codes
Memory corruption is a serious class of software vulnerabilities, which requires careful attention to be detected and removed from applications...
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Constructing Compartmental Models of Dynamic Systems Using a Software Package for Symbolic Computation in Julia
AbstractThis paper considers the problem of constructing compartmental models of dynamic systems by using a software package for symbolic calculation...
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Automated programming, symbolic computation, machine learning: my personal view
In this note, I present my personal view on the interaction of the three areas Automated Programming, Symbolic Computation, and Machine Learning....
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Extended homogeneous bivariate orthogonal polynomials: symbolic and numerical Gaussian cubature formula
An extension of the bivariate homogeneous orthogonal polynomials can be introduced by using a linear functional with complex moments obtained from...
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Automated discovery of symbolic laws governing skill acquisition from naturally occurring data
Skill acquisition is a key area of research in cognitive psychology as it encompasses multiple psychological processes. The laws discovered under...
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TGR: Neural-symbolic ontological reasoner for domain-specific knowledge graphs
Ontological reasoning has great prospects in applications based on domain-specific knowledge graphs (KG). However, it is difficult for existing logic...
