Modelling Functional Behavior of Event-based Systems: A Practical Knowledge-based Approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Functional behavior is considered to be the most basic, yet a critical notion in order to determine the characteristics of a system. However, how to reason about the functional behavior of a system in a systematic manner, is mostly limited by our cognitive processing abilities. While the UML-based behavior models can support a visual conceptualization of the functional behavior, they lack the rigorous, machine-processable reasoning capabilities. In this paper, we present a practical, knowledge-based approach to model the functional behavior that incorporates the notions of Commonsense Reasoning and Functional Reasoning over its core defining aspects. We demonstrate our approach with a detailed example, along with a set of use case scenarios. The main motivation behind this work was to develop a rigorous, logic-based approach to verify the levels of functional consistencies between cross-platform event-based systems. The focus of this paper, however, is to present the representational facility that can be utilized for the consistency validation system. While we provide a brief overview of the consistency validation system in this paper, a separate article will be dedicated for the comprehensive overview of the validation system itself.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it