Deriving Labels and Bisimilarity for Concurrent Constraint Programming

Aristizábal, Andrés; Bonchi, Filippo; Palamidessi, Catuscia; Pino, Luis; Valencia, Frank

doi:10.1007/978-3-642-19805-2_10

Cited by 18 publications

(70 citation statements)

References 28 publications

(38 reference statements)

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“…We are concerned with the verification of finite-state systems, thus we we shall dispense with the recursion operator which is meant for describing infinite behavior. We shall also omit the local/hiding operator for the simplicity of the presentation (see [3] for further details).…”

Section: Syntaxmentioning

confidence: 99%

“…In [3], the authors introduced a barbed semantics for ccp. Barbed equivalences have been introduced in [20] for CCS, and have become a classical way to define the semantics of formalisms equipped with unlabeled reduction semantics.…”

Section: Reduction Semanticsmentioning

confidence: 99%

“…It should be noticed that standard notion of observational ccp program equivalence was shown to coincide with≈ sb in the case of ccp-{+} [3]. For the sake of space we shall not introduce the standard notion-see [3] for further details.…”

Section: Definition 2 (Barbs)mentioning

confidence: 99%

“…The standard notion of observational (program) equivalence [26], roughly speaking, decrees that two ccp programs are observationally equivalent if each one can be replaced with the other in any ccp context and produce the same final stores. Other alternative notions of program equivalences for ccp such as saturated barbed bisimilarity (∼ sb ) and its weak variant (≈ sb ) were introduced in [3], where it is also shown that≈ sb coincides with the standard ccp observational equivalence for ccp-{+} programs.…”

Section: Introductionmentioning

confidence: 99%

“…The advantage of using these alternative notions of equivalence instead of using directly the standard notion of observational equivalence for ccp is that there is a substantial amount of work supporting the automatic verification of bisimilarity-based equivalence. In this paper we shall mainly deal with the verification of≈ sb for arbitrary ccp-{+} programs since, as mentioned above, ≈ sb coincides with observational program equivalence [3].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Efficient computation of program equivalence for confluent concurrent constraint programming

Pino

Bonchi

Valencia

2013

Proceedings of the 15th Symposium on Principles and Practice of Declarative Programming

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Concurrent Constraint Programming (ccp) is a well-established declarative framework from concurrency theory. Its foundations and principles e.g., semantics, proof systems, axiomatizations, have been thoroughly studied for over the last two decades. In contrast, the development of algorithms and automatic verification procedures for ccp have hitherto been far too little considered. To the best of our knowledge there is only one existing verification algorithm for the standard notion of ccp program (observational) equivalence. In this paper we first show that this verification algorithm has an exponential-time complexity even for programs from a representative sub-language of ccp; the summation-free fragment (ccp-{+}). We then significantly improve on the complexity of this algorithm by providing two alternative polynomial-time decision procedures for ccp-{+} program equivalence. Each of these two procedures has an advantage over the other. One has a better time complexity. The other can be easily adapted for the full language of ccp to produce significant state space reductions. The relevance of both procedures derives from the importance of ccp-{+}. This fragment, which has been the subject of many theoretical studies, has strong ties to first-order logic and an elegant denotational semantics, and it can be used to model real-world situations. Its most distinctive feature is that of confluence, a property we exploit to obtain our polynomial procedures.

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Section: Syntaxmentioning

confidence: 99%

Section: Reduction Semanticsmentioning

confidence: 99%