Conflict Detection in Call Control Using First-Order Logic Model Checking.
Bibliographic record
Abstract
Abstract. Feature interaction detection methods, whether online or offline, depend on previous knowledge of conflicts between the actions executed by the features. This knowledge is usually assumed to be given in the application domain. A method is proposed for identifying potential conflicts in call control actions, based on analysis of their pre/post-conditions. First of all, pre/postconditions for call processing actions are defined. Then, conflicts among the pre/post-conditions are defined. Finally, action conflicts are identified as a result of these conflicts. These cover several possibilities where the actions could be simultaneous or sequential. A first-order logic model-checking tool is used for automated conflict detection. As a case study, the APPEL call control language is used to illustrate the approach, with the Alloy tool serving as the model checker for automated conflict detection. This case study focuses on pre/post-conditions describing call control state and media state. The results of the method are evaluated by a domain expert with pragmatic understanding of the system’s behavior. The method, although computationally expensive, is fairly general and can be used to study conflicts in other domains. Keywords: Call control, conflict detection, feature interaction, policy, APPEL, Alloy, logic model checking.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".