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20202026
most citedMapping the Neuro-Symbolic AI Landscape by Architectures: A Handbook on Augmenting Deep Learning Through Symbolic Reasoning

4 citations · 11 across the 14 of their papers we have counts for

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Showing 2025Show all

7 papers · 1 filter

cs.AI2025

Satisfiability Modulo Theory Meets Inductive Logic Programming

Nijesh Upreti, Vaishak Belle

Inductive Logic Programming (ILP) provides interpretable rule learning in relational domains, yet remains limited in its ability to induce and reason with numerical constraints. Cl…

cs.AI2025

Lyria: A Genetic Algorithm-Driven Neuro-Symbolic Reasoning Framework for LLMs

Weizhi Tang, Kwabena Nuamah, Vaishak Belle

While LLMs have demonstrated impressive abilities across various domains, they struggle with two major issues. The first is that LLMs trap themselves into local optima and the seco…

cs.AI2025

HyGenar: An LLM-Driven Hybrid Genetic Algorithm for Few-Shot Grammar Generation

Weizhi Tang, Yixuan Li, Chris Sypherd +2

Grammar plays a critical role in natural language processing and text/code generation by enabling the definition of syntax, the creation of parsers, and guiding structured outputs.…

cs.CL2025

Incorporating Token Usage into Prompting Strategy Evaluation

Chris Sypherd, Sergei Petrov, Sonny George +1

In recent years, large language models have demonstrated remarkable performance across diverse tasks. However, their task effectiveness is heavily dependent on the prompting strate…

cs.AI2025★ 2 cited

Towards Developing Ethical Reasoners: Integrating Probabilistic Reasoning and Decision-Making for Complex AI Systems

Nijesh Upreti, Jessica Ciupa, Vaishak Belle

A computational ethics framework is essential for AI and autonomous systems operating in complex, real-world environments. Existing approaches often lack the adaptability needed to…

cs.AI2025

An Algebraic Framework for Hierarchical Probabilistic Abstraction

Nijesh Upreti, Vaishak Belle

Abstraction is essential for reducing the complexity of systems across diverse fields, yet designing effective abstraction methodology for probabilistic models is inherently challe…

cs.AI2025

Counterfactual Explanations as Plans

Vaishak Belle

There has been considerable recent interest in explainability in AI, especially with black-box machine learning models. As correctly observed by the planning community, when the ap…