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 intelligent behavior—from logical reasoning and sensory perception to emotional regulation and collective decision-making. This paper proposes 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 formally defined by a 9-tuple structure: O=(C,A,M,N,E,I,T,G,D), encapsulating its Components, Attributes, Methods, Neural components, Embedding, and governing Identity constraints, Trigger constraints, Goal constraints, and Constraint Daemons. To demonstrate the scope and versatility of COH, we formalize nine distinct intelligence types—including computational, perceptual, motor, affective, and embodied intelligence—each with detailed COH parameters and implementation blueprints. To operationalize the framework, we introduce GISMOL, a Python-based toolkit for instantiating COH objects and executing their constraint systems and neural components. GISMOL supports modular development and integration of intelligent agents, enabling a structured methodology for AGI system design. By unifying symbolic and connectionist paradigms within a constraint-governed architecture, COH provides a scalable and explainable foundation for building general purpose intelligent systems. A comprehensive summary of the research contributions is presented right after the introduction.