---
title: "Configuring Neuro-Symbolic Systems with Guaranteed Properties with Application in Hybrid Intelligence"
authors:
  - "Johannes E. Bendler"
doi: "10.24963/ijcai.2026/940"
url: "https://ijcai.org/proceedings/2026/940"
pdf: "https://ijcai.org/proceedings/2026/0940.pdf"
venue: "Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence"
year: 2026
pages: "8323-8324"
track: "Doctoral Consortium"
keywords:
  - "Knowledge Representation and Reasoning: Learning and reasoning"
  - "Humans and AI: Human-AI collaboration"
  - "Knowledge Representation and Reasoning: Automated reasoning and theorem proving"
  - "Machine Learning: Neuro-symbolic methods/Abductive Learning"
---

# Configuring Neuro-Symbolic Systems with Guaranteed Properties with Application in Hybrid Intelligence

Johannes E. Bendler

*Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence*, pages 8323-8324. DOI: [10.24963/ijcai.2026/940](https://doi.org/10.24963/ijcai.2026/940)

## Abstract

Neuro-symbolic systems are a promising paradigm for Hybrid Intelligence (HI), combining the complementary strengths of symbolic reasoning and machine learning in human-AI interactions. However, designing architectures that satisfy desired properties currently relies on informal reasoning, rather than a rigorous framework. Our research addresses this gap by developing a formal intermediate layer that links architecture design to system properties. Building on the visual pattern language of the Boxology for neuro-symbolic systems, we develop a mathematical formalization that is grounded in Category Theory, in order to enable proof-based guarantees of properties. The soundness of this formalism will be demonstrated through empirical case studies of existing Hybrid Intelligence systems. This work advances the theoretical foundations of neuro-symbolic AI by supporting principled design of HI systems.