Modelling Intelligent Systems for Smart Cities Using a Unified Framework for Intelligence and Intelligent Systems
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".