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Reactive, Generative and Stratified Models of Probabilistic Processes
TLDR
Three models of probabilistic processes, namely, reactive, generative, and stratified, are investigated within the context of PCCS, an extension of Milner′s SCCS in which each summand of a process summation expression is guarded by a probability and the sum of these probabilities is 1.
CCS expressions, finite state processes, and three problems of equivalence
TLDR
It is shown that observation equivalence can be tested in cubic time and is the limit of a sequence of equivalence notions, and that, even for a very restricted type of process, testing for failure equivalence is PSPACE-complete.
Reactive, generative, and stratified models of probabilistic processes
TLDR
A structural operational semantics of PCCS is given as a set of inference rules for each of the models, a notion of bisimulation semantics, and some conference proofs are presented.
Simple Linear-Time Algorithms for Minimal Fixed Points (Extended Abstract)
TLDR
This work presents global and local algorithms for evaluating minimal fixed points of dependency graphs, a general problem in fixed-point computation and model checking, matching the complexity of the best existing algorithms for similar problems, and are simple to understand.
Efficient Model Checking Using Tabled Resolution
TLDR
XMC is presented, an XSB-based local model checker for a CCS-like value-passing language and the alternation-free fragment of the modal mu-calculus, written in under 200 lines of XSB code.
Algebraic Reasoning for Probabilistic Concurrent Systems
TLDR
The nondeterministic process summation operator of SCCS is replaced with a probabilistic one, in which the probability of behaving like a particular summand is given explicitly, to obtain a calculus, PCCS, for reasoning about communicating Probabilistic processes.
Monte Carlo Model Checking
TLDR
What is believed to be the first randomized, Monte Carlo algorithm for temporal-logic model checking is presented, given a specification S of a finite-state system, an LTL formula ϕ, and parameters e and δ, which takes random samples from the Buchi automaton B.
Axiomatizing Probabilistic Processes: ACP with Generative Probabilities
TLDR
This paper obtains the axiom system prACP I −- , a probabilistic version of ACP which can be used to reason algebraically about the reliability and performance of concurrent systems.
Equivalences, Congruences, and Complete Axiomatizations for Probabilistic Processes
TLDR
It is shown that, unlike nondeterministic transition systems, “maximality” of traces and failures does not increase the distinguishing power of trace and failure equivalence, respectively.
Model Repair for Probabilistic Systems
TLDR
Using a new version of parametric probabilistic model checking, it is shown how the Model Repair problem can be reduced to a nonlinear optimization problem with a minimal-cost objective function, thereby yielding a solution technique.
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