27 research outputs found
A sound and complete proof system for probabilistic processes
n this paper we present a process algebra model of probabilistic communicating processes based on classical CSP. To define our model we have replaced internal non-determinism by generative probabilistic choices, and external non-determinism by reactive probabilistic choices, with the purpose of maintaining the meaning of the classical CSP operators, once generalized in a probabilistic way. Thus we try to keep valid, as far as possible, the laws of CSP. This combination of both internal and external choice makes strongly difficult the definition of a probabilistic version of CSP. In fact, we can find in the current literature quite a number of papers on probabilistic processes, but only in a few of them internal and external choices are combined, trying to preserve their original meaning.
Starting with a denotational semantics where the corresponding domain is a set of probabilistic trees with two kinds of nodes, representing the internal and external choices, we define a sound and complete proof system, with very similar laws to those of the corresponding CSP
Uniform Labeled Transition Systems for Nondeterministic, Probabilistic, and Stochastic Process Calculi
Labeled transition systems are typically used to represent the behavior of
nondeterministic processes, with labeled transitions defining a one-step state
to-state reachability relation. This model has been recently made more general
by modifying the transition relation in such a way that it associates with any
source state and transition label a reachability distribution, i.e., a function
mapping each possible target state to a value of some domain that expresses the
degree of one-step reachability of that target state. In this extended
abstract, we show how the resulting model, called ULTraS from Uniform Labeled
Transition System, can be naturally used to give semantics to a fully
nondeterministic, a fully probabilistic, and a fully stochastic variant of a
CSP-like process language.Comment: In Proceedings PACO 2011, arXiv:1108.145
Testing Reactive Probabilistic Processes
We define a testing equivalence in the spirit of De Nicola and Hennessy for
reactive probabilistic processes, i.e. for processes where the internal
nondeterminism is due to random behaviour. We characterize the testing
equivalence in terms of ready-traces. From the characterization it follows that
the equivalence is insensitive to the exact moment in time in which an internal
probabilistic choice occurs, which is inherent from the original testing
equivalence of De Nicola and Hennessy. We also show decidability of the testing
equivalence for finite systems for which the complete model may not be known
Metric Semantics and Full Abstractness for Action Refinement and Probabilistic Choice
This paper provides a case-study in the field of metric semantics for probabilistic programming. Both an operational and a denotational semantics are presented for an abstract process language L_pr, which features action refinement and probabilistic choice. The two models are constructed in the setting of complete ultrametric spaces, here based on probability measures of compact support over sequences of actions. It is shown that the standard toolkit for metric semantics works well in the probabilistic context of L_pr, e.g. in establishing the correctness of the denotational semantics with respect to the operational one. In addition, it is shown how the method of proving full abstraction --as proposed recently by the authors for a nondeterministic language with action refinement-- can be adapted to deal with the probabilistic language L_pr as well
Characterising Testing Preorders for Finite Probabilistic Processes
In 1992 Wang & Larsen extended the may- and must preorders of De Nicola and
Hennessy to processes featuring probabilistic as well as nondeterministic
choice. They concluded with two problems that have remained open throughout the
years, namely to find complete axiomatisations and alternative
characterisations for these preorders. This paper solves both problems for
finite processes with silent moves. It characterises the may preorder in terms
of simulation, and the must preorder in terms of failure simulation. It also
gives a characterisation of both preorders using a modal logic. Finally it
axiomatises both preorders over a probabilistic version of CSP.Comment: 33 page
On Generative Parallel Composition
A major reason for studying probabilistic processes is to establish a link between a formal model for describing functional system behaviour and a stochastic process. Compositionality is an essential ingredient for specifying systems. Parallel composition in a probabilistic setting is complicated since it gives rise to non-determinism, for instance due to interleaving of independent autonomous activities. This paper presents a detailed study of the resolution of non-determinism in an asynchronous generative setting. Based on the intuition behind the synchronous probabilistic calculus PCCS we formulate two criteria that an asynchronous parallel composition should fulfill. We provide novel probabilistic variants of parallel composition for CCS and CSP and show that these operators satisfy these general criteria, opposed to most existing proposals. Probabilistic bisimulation is shown to be a congruence for these operators and their expansion is addressed.\ud
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We would like to thank the reviewers for their constructive criticism and for pointing out the relation between BPTSs and the model of Pnueli and Zuck. We also thank Ed Brinksma and Rom Langerak (both of the University of Twente) for fruitful discussions
A uniform framework for modelling nondeterministic, probabilistic, stochastic, or mixed processes and their behavioral equivalences
Labeled transition systems are typically used as behavioral models of concurrent processes, and the labeled transitions define the a one-step state-to-state reachability relation. This model can be made generalized by modifying the transition relation to associate a state reachability distribution, rather than a single target state, with any pair of source state and transition label. The state reachability distribution becomes a function mapping each possible target state to a value that expresses the degree of one-step reachability of that state. Values are taken from a preordered set equipped with a minimum that denotes unreachability. By selecting suitable preordered sets, the resulting model, called ULTraS from Uniform Labeled Transition System, can be specialized to capture well-known models of fully nondeterministic processes (LTS), fully
probabilistic processes (ADTMC), fully stochastic processes (ACTMC), and of nondeterministic and probabilistic (MDP) or nondeterministic and stochastic (CTMDP) processes. This uniform treatment of different behavioral models extends to behavioral equivalences. These can be defined on ULTraS by relying on appropriate measure functions that expresses the degree of reachability of a set of states when performing
single-step or multi-step computations. It is shown that the specializations of bisimulation, trace, and testing
equivalences for the different classes of ULTraS coincide with the behavioral equivalences defined in the literature over traditional models