Unified Modelling of Intelligent Robotic Systems: Applications of COH/GISMOL in Automation Engineering
Bibliographic record
Abstract
Robotics and automation increasingly demand intelligent systems that integrate perception, cognition, and action while assuring safety and compliance in dynamic environments. Existing paradigms-behavior-based control, layered architectures, middleware frameworks, and learning-based controllers-solve parts of the problem but rarely offer a cohesive way to model structure, learning, and constraints together. This paper introduces Constrained Object Hierarchies (COH) and its Python toolkit GISMOL (General Intelligent System Modelling Language) as a unified approach to designing and implementing intelligent systems. COH formalizes intelligent systems via a 9-tuple representation-components, attributes, methods, neural components, embeddings, identity constraints, trigger constraints, goal constraints, and constraint daemons -thereby separating what a system is from what it does and what must never be violated. Neuroscience-inspired hierarchical processing motivates this decomposition and the separation of learned behaviors from innate constraints. We demonstrate COH/GISMOL on five representative cases in robotics and automation: warehouse AMR, collaborative assembly, autonomous harvesting, predictive maintenance, and multi-robot search-and-rescue. Across these cases, COH/GISMOL delivers: (i) systematic modelling, (ii) integrated learning with safety-first constraint enforcement, and (iii) maintainable hierarchies that support verification and runtime monitoring consistent with emerging standards and best practices.
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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 | 0.003 |
| 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 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".