12 papers
EM-NeSy: Expectation Maximization for Neurosymbolic Learning
Annegret Seibt, Luc De Raedt, Giuseppe Marra
Neurosymbolic (NeSy) models integrate neural networks and symbolic reasoning for robust and interpretable AI. State-of-the-art NeSy models require that the symbolic component is ex…
DeepLog: A Software Framework for Modular Neurosymbolic AI
Robin Manhaeve, Stefano Colamonaco, Vincent Derkinderen +4
DeepLog is an operational neurosymbolic framework that unifies logic and deep learning within standard PyTorch workflows. While existing neurosymbolic systems focus on a particular…
The DeepLog Neurosymbolic Machine
Vincent Derkinderen, Robin Manhaeve, Rik Adriaensen +4
We contribute a theoretical and operational framework for neurosymbolic AI called DeepLog. DeepLog introduces building blocks and primitives for neurosymbolic AI that make abstract…
DeepProofLog: Efficient Proving in Deep Stochastic Logic Programs
Ying Jiao, Rodrigo Castellano Ontiveros, Luc De Raedt +4
Neurosymbolic (NeSy) AI aims to combine the strengths of neural architectures and symbolic reasoning to improve the accuracy, interpretability, and generalization capability of AI…
LexiCon: a Benchmark for Planning under Temporal Constraints in Natural Language
Periklis Mantenoglou, Rishi Hazra, Pedro Zuidberg Dos Martires +1
Owing to their reasoning capabilities, large language models (LLMs) have been evaluated on planning tasks described in natural language. However, LLMs have largely been tested on p…
DeepGraphLog for Layered Neurosymbolic AI
Adem Kikaj, Giuseppe Marra, Floris Geerts +2
Neurosymbolic AI (NeSy) aims to integrate the statistical strengths of neural networks with the interpretability and structure of symbolic reasoning. However, current NeSy framewor…