A neurosymbolic perspective on LLMs

Differentiable probabilistic answer set programming

A collection of neuro-symbolic systems, papers and videos

Graph Learning for PDDL Planning

Code for the ICLR 2024 paper "How Realistic Is Your Synthetic Data? Constraining Deep Generative Models for Tabular Data"

Official repository for CoTran: An LLM-based code translator for whole-program translation, fine-tuned using feedback from compiler and symbolic execution

Unleash the fuzz on your C codebase.

An intelligent, multi-agent framework for artificial biome simulation.

An advanced tool for generating, visualizing, and evaluating domain-specific knowledge graphs and ontologies.

A list of awesome resources related to constraint learning

Code and data of "NeSIG: A Neuro-Symbolic Method for Learning to Generate Planning Problems"

A dedicated repository for learning and researching about neuro-symbolic artificial intelligence (NSAI)

headquarters of the April team in Edinburgh

An experimental codebase developed to empirically investigate several phenomena predicted by the theoretical framework proposed in the associated research paper, grounded in the Free Energy Principle

Modular framework for building self-modeling artificial agents with explicit internal state representation and meta-cognitive capabilities. Includes RL, hybrid, and dummy policies with integrated SelfModel monitoring and scientific metrics.

ANSR-DT: An open-source Adaptive Neuro-Symbolic framework for real-time learning and reasoning in Digital Twins. Combines CNN-LSTM, reinforcement learning, and symbolic reasoning for interpretable, adaptive decision-making.

LLY-HDC uses hyperdimesnional computing and neurosymbolic AI to implement a high-performance AI model on quantum computers

My BSc Thesis on fuzzing automation through LLMs.

Variants of the Link Prediction datasets DB100k, YAGO3-10 and NELL-995 with their related entity types, type hierarchies and predicates domains and ranges

AI UK Fringe Event on AI research at City, University of London