4 citations · 11 across the 14 of their papers we have counts for
14 papers
Integrating LTL Constraints into PPO for Safe Reinforcement Learning
Maifang Zhang, Hang Yu, Qian Zuo +3
This paper proposes Proximal Policy Optimization with Linear Temporal Logic Constraints (PPO-LTL), a framework that integrates safety constraints written in LTL into PPO for safe r…
Propositional Abduction via Only-Knowing: A Non-Monotonic Approach
Sanderson Molick, Vaishak Belle
The paper introduces a basic logic of knowledge and abduction by extending Levesque logic of only-knowing with an abduction modal operator defined via the combination of basic epis…
Logic Tensor Network-Enhanced Generative Adversarial Network
Nijesh Upreti, Vaishak Belle
In this paper, we introduce Logic Tensor Network-Enhanced Generative Adversarial Network (LTN-GAN), a novel framework that enhances Generative Adversarial Networks (GANs) by incorp…
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…
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…
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.…
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…
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…
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…
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…
Practical Considerations for Agentic LLM Systems
Chris Sypherd, Vaishak Belle
As the strength of Large Language Models (LLMs) has grown over recent years, so too has interest in their use as the underlying models for autonomous agents. Although LLMs demonstr…
Mapping the Neuro-Symbolic AI Landscape by Architectures: A Handbook on Augmenting Deep Learning Through Symbolic Reasoning
Jonathan Feldstein, Paulius Dilkas, Vaishak Belle +1
Integrating symbolic techniques with statistical ones is a long-standing problem in artificial intelligence. The motivation is that the strengths of either area match the weaknesse…
Neural Networks Decoded: Targeted and Robust Analysis of Neural Network Decisions via Causal Explanations and Reasoning
Alec F. Diallo, Vaishak Belle, Paul Patras
Despite their success and widespread adoption, the opaque nature of deep neural networks (DNNs) continues to hinder trust, especially in critical applications. Current interpretabi…
Generating Random Logic Programs Using Constraint Programming
Paulius Dilkas, Vaishak Belle
Testing algorithms across a wide range of problem instances is crucial to ensure the validity of any claim about one algorithm's superiority over another. However, when it comes to…