---
title: "NeSyA: Neurosymbolic Automata"
authors:
  - "Nikolaos Manginas"
  - "George Paliouras"
  - "Luc De Raedt"
doi: "10.24963/ijcai.2025/662"
url: "https://ijcai.org/proceedings/2025/662"
pdf: "https://ijcai.org/proceedings/2025/0662.pdf"
venue: "Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"
year: 2025
pages: "5950-5958"
track: "Main Track"
keywords:
  - "Machine Learning: ML: Neuro-symbolic methods/Abductive Learning"
  - "Knowledge Representation and Reasoning: KRR: Learning and reasoning"
---

# NeSyA: Neurosymbolic Automata

Nikolaos Manginas, George Paliouras, Luc De Raedt

*Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence*, pages 5950-5958. DOI: [10.24963/ijcai.2025/662](https://doi.org/10.24963/ijcai.2025/662)

## Abstract

Neurosymbolic (NeSy) AI has emerged as a promising direction to integrate
neural and symbolic reasoning. Unfortunately, little effort has been given 
to developing NeSy systems tailored to sequential/temporal problems. We identify
symbolic automata (which combine the power of automata for temporal reasoning
with that of propositional logic for static reasoning) as a suitable formalism for 
expressing knowledge in temporal domains. Focusing on the task of sequence classification
and tagging we show that symbolic automata can be integrated with neural-based
perception, under probabilistic semantics towards an end-to-end differentiable model. 
Our proposed hybrid model, termed NeSyA (Neuro Symbolic 
Automata) is shown to either scale or perform more accurately than previous
NeSy systems in a synthetic benchmark and to provide benefits in terms of generalization
compared to purely neural systems in a real-world event recognition task.
Code is available at: https://github.com/nmanginas/nesya