Bibliographic record
Abstract
Model checking is an automated technique for the verification of finite-state systems that is widely used in practice. In Bounded Model Checking (BMC) the system is checked only until a given execution depth from the initial state. State of the art model checkers apply Binary Decision Diagrams (BDDs) as well as Satisfiability Solving (SAT) for this task. However, both methods suffer from the state explosion problem, which restricts the application of model checking to only modestly sized systems. The importance of model checking makes it worthwhile to explore alternative technologies, in the hope of enabling the application of the technique to a wider class of systems. Description Logic (DL) is a family of knowledge representation formalisms, mainly used for designing ontologies, for which reasoning is based on tableaux techniques. In this article, we show how model checking problems can be solved using DL reasoning. We present two different encodings of a model checking problem as a consistency check in DL, and show how DL can serve as a natural setting for representing and solving a BMC problem. Experimental results, using the DL reasoner FaCT++, give encouraging results.
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".