Expressing Priorities and External Probabilities in Process Algebra via Mixed Open/Closed Systems

  • Authors:
  • Mario Bravetti

  • Affiliations:
  • Dipartimento di Scienze dell'Informazione, Università di Bologna, Bologna, Italy

  • Venue:
  • Electronic Notes in Theoretical Computer Science (ENTCS)
  • Year:
  • 2008

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Abstract

Defining operational semantics for a process algebra is often based either on labeled transition systems that account for interaction with a context or on the so-called reduction semantics: we assume to have a representation of the whole system and we compute unlabeled reduction transitions (leading to a distribution over states in the probabilistic case). In this paper we consider mixed models with states where the system is still open (towards interaction with a context) and states where the system is already closed. The idea is that (open) parts of a system ''P'' can be closed via an operator ''P@6G'' that turns already synchronized actions whose ''handle'' is specified inside ''G'' into prioritized reduction transitions (and, therefore, states performing them into closed states). We show that we can use the operator ''P@6G'' to express multi-level priorities and external probabilistic choices (by assigning weights to handles inside G), and that, by considering reduction transitions as the only unobservable @t transitions, the proposed technique is compatible, for process algebra with general recursion, with both standard (probabilistic) observational congruence and a notion of equivalence which aggregates reduction transitions in a (much more aggregating) trace based manner. We also observe that the trace-based aggregated transition system can be obtained directly in operational semantics and we present the ''aggregating'' semantics. Finally, we discuss how the open/closed approach can be used to also express discrete and continuous (exponential probabilistic) time and we show that, in such timed contexts, the trace-based equivalence can aggregate more with respect to traditional lumping based equivalences over Markov Chains.