GISMOL: A General Intelligent Systems Modelling Language

This paper introduces GISMOL (General Intelligent Systems Modelling Language), a Python library under active development for modeling and prototyping general intelligent systems based on the Constrained Object Hierarchies (COH) theoretical framework. COH provides a neuroscience-inspired 9-tuple model that integrates symbolic constraints with neural computation, addressing limitations in current AI paradigms that often separate statistical learning from symbolic reasoning. GISMOL aims to operationalize COH through modular components supporting hierarchical object composition, constraint-aware neural networks, multi-domain reasoning engines, and natural language understanding with constraint validation. To illustrate its potential, we present six conceptual case studies spanning healthcare, smart manufacturing, autonomous drone delivery, finance, governance, and education. These examples demonstrate how GISMOL can translate COH theory into executable prototypes that prioritize safety, compliance, and adaptability in solving complex real-world problems. Preliminary comparative analysis suggests GISMOL’s promise in explainability, modularity, and cross-domain applicability relative to existing frameworks. This work contributes both a theoretical foundation for neuro-symbolic integration and an evolving practical toolkit that seeks to bridge the gap between AGI theory and deployable intelligent systems.