# Deriving Symbolic Representations from Stochastic Process Algebras

@inproceedings{Kuntz2002DerivingSR, title={Deriving Symbolic Representations from Stochastic Process Algebras}, author={Matthias Kuntz and Markus Siegle}, booktitle={PAPM-PROBMIV}, year={2002} }

A new denotational semantics for a variant of the stochastic process algebra TIPP is presented, which maps process terms to Multiterminal binary decision diagrams. It is shown that the new semantics is Markovian bisimulation equivalent to the standard SOS semantics. The paper also addresses the difficult question of keeping the underlying state space minimal at every construction step.

## 16 Citations

### An Efficient Symbolic Elimination Algorithm for the Stochastic Process Algebra Tool CASPA

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This paper focuses on an extension of CASPA's modelling language by weighted immediate actions, and presents an efficient symbolic algorithm for the elimination of vanishing states.

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This thesis contributes to the alleviation of the state space explosion problem by devising a new denotational semantics for stochastic process algebras that relies on efficient data structures for the compact representation of even huge state spaces.

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- Computer ScienceValidation of Stochastic Systems
- 2004

This paper describes symbolic techniques for the construction, representation and analysis of large, probabilistic systems, focusing on BDDs, multi-valued decision diagrams (MDDs), multi-terminal binary decision diagrams(MTBDDs) and matrix diagrams.

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This paper describes the tool CASPA, a new performance evaluation tool which is based on a Markovian stochastic process algebra and presents several case studies which demonstrate the superiority of CASPA over sparse-matrix-based process algebra tools.

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### Faster SPDL Model Checking Through Property-Driven State Space Generation

- Computer ScienceEPEW
- 2007

This paper proposes an approach that directly generates the reduced PTS from a given SPA specification and an SPDL path formula, and shows enormous state space reductions, at no increase in generation time.

### Scalable Stochastic Modelling for Resilience

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This chapter summarises techniques that are suitable for performance and resilience modelling and analysis of massive stochastic systems. We will introduce scalable techniques that can be applied to…

### SPDL Model Checking via Property-Driven State Space Generation

- Computer Science
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In this report we describe how both, memory and time requirements for stochastic model checking of SPDL (stochastic propositional dynamic logic) formulae can significantly be reduced. SPDL is the…

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