S-AI-CITY: A HORMONAL ARCHITECTURE FOR PARSIMONIOUS URBAN INTELLIGENCE

Abstract

Smart cities increasingly rely on heterogeneous and interdependent digital infrastructures combining mobility, energy, environmental monitoring, water and waste management, cybersecurity, telecommunications, public services, and critical infrastructure supervision. Although specialized artificial intelligence components can provide high performance within individual domains, city-scale intelligence raises a broader architectural problem: how to determine which capabilities should be activated, how to coordinate them across domains, how to prevent persistent activation of unnecessary computational resources, and how to preserve an interpretable memory of the context that produced each operational response. This article introduces S-AI-CITY, a domain-specific instantiation of the Sparse Artificial Intelligence (S-AI) paradigm for adaptive urban systems. The proposed architecture is based on four complementary principles: activation parsimony, artificial hormonal orchestration, functionally specialized urban agents, and persistent symbolic memory. Six canonical urban hormones are introduced: Mobilin, encoding mobility and transportation stress; Energexin, representing energy and resource pressure; Environin, representing environmental and utility stress; Resiliencin, encoding infrastructure failure, emergency, and recovery pressure; Normin, representing cybersecurity, privacy, trust, and compliance stress; and Servicin, representing public-service demand, collective-event pressure, accessibility, and citizen-facing service load. Urban Gland Agents transform normalized observations into bounded hormonal emissions. A Hormonal Engine integrates these emissions, applies decay, crossregulation, and topology-dependent propagation, and produces the shared hormonal state 𝐻(𝑡). The CityMetaAgent then uses this state, available resources, operational constraints, and symbolic memory to activate a contextually sufficient subset of specialized urban agents rather than maintaining the complete agent repertoire continuously active. Significant decision episodes are stored as Urban Engrams, preserving the hormonal context, activated agents, actions, resource conditions, and observed outcomes for subsequent retrieval, audit, and behavioral reuse. The architecture is deployed hierarchically across zone, district, and city levels to support both global coordination and local autonomy under connectivity degradation. This first article develops the theoretical and bio-inspired foundations of S-AI-CITY and specifies its global architecture. Article II provides the complete agent specification, mathematical formulation of multi-layer hormonal dynamics, stability analysis, discretization, parsimonious orchestration, and symbolic-memory formalism. Article III develops the SAI-UT+ CITY experimental testbench and evaluates the framework through multi-stress urban scenarios, baseline comparisons, ablation studies, robustness analysis, and scalability experiments.