Constrained Object Hierarchies as a Unified Theoretical Model for Intelligence and Intelligent Systems

Achieving Artificial General Intelligence (AGI) requires a unified framework capable of modeling the full spectrum of intelligence—from logical reasoning and sensory perception to emotional regulation and collective behavior. This paper introduces Constrained Object Hierarchies (COH), a neuroscience-inspired theoretical model that represents intelligent systems as hierarchical compositions of objects governed by symbolic structure, neural adaptation, and constraint-based control. Each object is defined by a 9-tuple O = (C, A, M, N, E, I, T, G, D), encapsulating its Components, Attributes, Methods, Neural components, Embedding, and governing Identity, Trigger, Goal, and Daemon constraints. We demonstrate COH’s expressiveness by formalizing 19 distinct intelligence types—including human-centric, artificial, and collective intelligences—each with detailed COH parameters and implementation blueprints. These formalizations span logical-mathematical, linguistic, spatial, emotional, social, computational, perceptual, motor, and embodied intelligences, among others. To bridge theory and practice, we introduce GISMOL, a Python-based toolkit for instantiating COH objects and executing their constraint systems and neural components. GISMOL enables modular development and integration of intelligent agents, supporting a structured methodology for AGI system design. By unifying symbolic and connectionist paradigms under a constraint-governed architecture, COH provides a scalable foundation for building general-purpose intelligent systems.