Model checking nondeterministic and randomly timed systems
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
Quantitative model checking has become an indispensable tool to analyze performance and dependability characteristics such as the expected round trip time in a packet switched network or the failure probability of a safety-critical system. So far, the existing model checking techniques lack support for models which combine stochastic timing and nondeterminism. This is surprising, as nondeterminism is the key for compositional modeling and occurs naturally in distributed systems. In this thesis, we overcome this limitation. More precisely, we consider continuous-time Markov decision processes (CTMDPs), a model which closely entangles stochasticity and nondeterminism. Our main contribution is a discretization which allows to compute the maximum and minimum probability to enter a set of goal states in a CTMDP within a given time-bound. By applying value iteration techniques to the induced discrete-time model, we compute the desired probabilities up to an a priori specified precision. This result provides the basis for model checking important performance and dependability characteristics and has been extended to a variety of other nondeterministic and randomly timed system models. We demonstrate the applicability of our techniques by a number of case studies which also show that nondeterministic modeling makes an essential difference in the area of performance and dependability evaluation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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