Modelling Intelligent Systems for Smart Cities Using a Unified Framework for Intelligence and Intelligent Systems

The rapid urbanization and increasing complexity of modern cities demand intelligent systems that can adapt, learn, and operate safely within complex constraints. This paper presents Constrained Object Hierarchies (COH), a neuroscience-grounded theoretical framework for artificial general intelligence, and its implementation in GISMOL (General Intelligent System Modelling Language), as a unified approach for modelling and implementing intelligent systems for Smart Cities. We demonstrate how COH’s 9-tuple formalization (Components, Attributes, Methods, Neural components, Embedding, Identity constraints, Trigger constraints, Goal constraints, and Constraint daemons) provides a comprehensive foundation for building complex intelligent systems. Through three detailed case studies-Intelligent Adaptive Traffic Management, Predictive Maintenance for City Infrastructure, Dynamic Public Resource Allocation-we show how COH/GISMOL enables the development of constraint-aware, hierarchically organized, and neurally enhanced systems that address critical urban challenges. The framework’s ability to integrate neural components with explicit constraint management, hierarchical reasoning, and natural language processing represents a significant advancement over existing approaches.