Conditional Commitments
Bibliographic record
Abstract
While modeling interactions using social commitments provides a fundamental basis for capturing flexible and declarative interactions and helps in addressing the challenge of ensuring compliance with specifications, the designers of the system cannot guarantee that an agent complies with its commitments as it is supposed to, or at least an agent doesn't want to violate its commitments. They may still wish to develop efficient and scalable algorithms by which model checking conditional commitments, a natural and universal frame of social commitments, is feasible at design time. However, distinguishing between different but related types of conditional commitments, and developing dedicated algorithms to tackle the problem of model checking conditional commitments, is still an active research topic. In this article, we develop the temporal logic CTL cc that extends Computation Tree Logic (CTL) with new modalities which allow representing and reasoning about two types of communicating conditional commitments and their fulfillments using the formalism of interpreted systems. We introduce a set of rules to reason about conditional commitments and their fulfillments. The verification technique is based on developing a new symbolic model checking algorithm to address this verification problem. We analyze the computational complexity and present the full implementation of the developed algorithm on top of the MCMAS model checker. We also evaluate the algorithm's effectiveness and scalability by verifying the compliance of the NetBill protocol, taken from the business domain, and the process of breast cancer diagnosis and treatment, taken from the health-care domain, with specifications expressed in CTL cc . We finally compare the experimental results with existing proposals.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".