Work

Encoding Higher-Order Logic in Spatio-Temporal Hypergraphs for Neuro-Symbolic Learning

Year: 2025

Type: conference-paper

Abstract: This work integrates Monadic Second-Order (MSO) logic into Spatio-Temporal Heterogeneous Hypergraphs (STHH) to advance Neuro-Symbolic AI.By bridging higher-ordered symbolic logic with neural computations, STHH offers a novel framework for knowledge representation and learning.Evaluations on a custom agricultural dataset show that the proposed STHH outperforms state-of-the-art hypergraph models across F1-score, accuracy, and AUC metrics.Despite challenges such as limited standardized datasets, this study underscores the potential of integrating higher-ordered symbolic logic into neural systems to achieve robust and interpretable AI.

Source: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)

DOI: 10.14428/esann/2025.es2025-78

Authors: Bikram Pratim BHUYAN, Amylia AIT SAADI, Amar RAMDANE-CHERIF

Institutions: Université Paris-Saclay, ESME - École d’ingénieurs pluridisciplinaires, Laboratoire d'Ingénierie des Systèmes de Versailles

Language: English

Topic: Logic, Reasoning, and Knowledge

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze

Funder: Agence Nationale de la Recherche

Awards: G2657152313, Idex Paris-Saclay, G4866930200