Probabilistic Guarded Commands Mechanized in HOL
Bibliographic record
Abstract
The probabilistic guarded-command language pGCL [Carroll Morgan, Annabelle McIver. pGCL: formal reasoning for random algorithms. South African Computer Journal (1999)] contains both demonic and probabilistic nondeterminism, which makes it suitable for reasoning about distributed random algorithms [Carroll Morgan. Proof rules for probabilistic loops. In Proceedings of the BCS-FACS 7th Refinement Workshop. He Jifeng, John Cooke and Peter Wallis (eds). Springer Verlag Workshops in Computing, 1996]. Proofs are based on weakest precondition semantics, using an underlying logic of real- (rather than Boolean-) valued functions. We present a mechanization of the quantitative logic for pGCL [Carroll Morgan, Annabelle McIver, and Karen Seidel, Probabilistic predicate transformers. ACM Transactions on Programming Languages and Systems, 18(3): 325–353, May 1996] using the HOL theorem prover [M.J.C. Gordon and T.F. Melham. Introduction to HOL (A theorem-proving environment for higher order logic). Cambridge University Press, 1993], including a proof that all pGCL commands satisfy the new condition sublinearity, the quantitative generalization of conjunctivity for standard GCL [E.W. Dijkstra. A Discipline of Programming. Prentice Hall, 1976]. The mechanized theory also supports the creation of an automatic proof tool which takes as input an annotated pGCL program and its partial correctness specification, and derives from that a sufficient set of verification conditions. This is employed to verify the partial correctness of the probabilistic voting stage in Rabin's mutual-exclusion algorithm [Eyal Kushilevitz and Michael O. Rabin. Randomized mutual exclusion algorithms revisited. In Maurice Herlihy, editor, Proceedings of the 11th Annual Symposium on Principles of Distributed Computing, pages 275–283, Vancouver, BC, Canada, August 1992. ACM Press].
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.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".