𝔖 Bobbio Scriptorium
✦   LIBER   ✦

Completeness of fair ASM refinement

✍ Scribed by Gerhard Schellhorn


Publisher
Elsevier Science
Year
2011
Tongue
English
Weight
321 KB
Volume
76
Category
Article
ISSN
0167-6423

No coin nor oath required. For personal study only.

✦ Synopsis


ASM refinements are verified using generalized forward simulations which allow us to refine m abstract operations to n concrete operations with arbitrary m and n. One main difference from data refinement is that ASM refinement considers infinite runs and termination. Since backward simulation does not preserve termination in general, the standard technique of adding history information to the concrete level is not applicable to get a completeness proof. The power set construction also adds infinite runs and is therefore not applicable either. This paper shows that a completeness proof is nevertheless possible by adding infinite prophecy information, effectively moving nondeterminism to the initial state. Adding such prophecy information can be done not only on the semantic level, but also by a simple syntactic transformation that removes the choose construct of ASMs. The completeness proof is also translated to a completeness proof for IO automata. Finally, the proof is extended to deal with supplementary predicates, that specify fairness and liveness assumptions, by transferring a related result of Wim Hesselink for refinements that use the Abadi-Lamport setting.


πŸ“œ SIMILAR VOLUMES


Refinement of fair action systems
✍ Ralph J.R. Back; Qiwen Xu πŸ“‚ Article πŸ“… 1998 πŸ› Springer-Verlag 🌐 English βš– 243 KB
Fair Hamilton Decompositions of Complete
✍ C.D. Leach; C.A. Rodger πŸ“‚ Article πŸ“… 2002 πŸ› Elsevier Science 🌐 English βš– 88 KB

A fair hamilton decomposition of the complete multipartite graph G is a set of hamilton cycles in G whose edges partition the edges of G in such a way that, for each pair of parts and for each pair of hamilton cycles H 1 and H 2 , the difference in the number of edges in H 1 and H 2 joining vertices