M. Siegle. Advances in Model Representations. In Proc. PAPM/PROBMIV 2001, LNCS Volume 2165, Aachen, Germany, 2001. Springer Verlag.  Home/Search   Document Details and Download   Summary   Related Articles   Check

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M. Siegle. Advances in model representation. In L. de Alfaro and S. Gilmore, editors, Process Algebra and Probabilistic Methods, Joint Int. Workshop PAPMPROBMIV 2001.

.... stochastic models have been represented symbolically with the help of Multi terminal BDDs (MTBDD) and it has been shown that in addition to functional analysis, performance analysis and the verification of performability properties can also be carried out on such symbolic representations [7, 14, 19, 21, 25, 27]. We employ stochastic process algebras (SPA) for model specification and wish to generate symbolic representations directly from the high level model, instead of generating transition systems as an intermediate representation. For this purpose, we develop a denotational semantics which maps a ....

....as an intermediate representation. For this purpose, we develop a denotational semantics which maps a given SPA specification directly to its underlying MTBDD. The semantics proceeds in a compositional fashion, according to the structure of the process term at hand, as it has been observed before [7, 14, 27] that structure exploitation is the key to achieving compact representations. The process algebra which we use is a restricted version of TIPP [15] which guarantees finiteness of the underlying state space. To our knowledge, this is the first complete BDD based semantics for SPA which completely ....

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M. Siegle. Advances in model representation. In L. de Alfaro and S. Gilmore, editors, Process Algebra and Probabilistic Methods, Joint Int. Workshop PAPMPROBMIV 2001, pages 1--22. Springer, LNCS 2165, September 2001.

.... Producer Consumer Figure 3: Hierarchic composition of the PC submodels via Join 3 Symbolic representation of state spaces It is known that MTBDDs can be employed for representing large state spaces very compactly, provided that the overall state space is built in a compositional fashion [Sie01] Before going into detail, we briefly recapitulate the basics. MTBDDs [FMY97, BFG 97] are an extension of BDDs [Bry86] for the graphbased representation of pseudo Boolean functions, i.e. functions of type IB n IR. An MTBDD is a collapsed binary decision tree whose isomorphic subtrees ....

....algorithm [FMY97] Fig. 4 shows an example stochastic LTS represented by an MTBDD, where a dashed (solid) line represents the Boolean values 0 (1) of the corresponding This interleaved ordering is the commonly accepted heuristics for obtaining small MTBDD sizes, see for instance [EFT93, FMY97, Sie01] 00 11 01 10 t2 s2 a s1 t1 # enq deq 0 a action enq, # enq, # enq, # deq, deq, deq, Figure 4: Stochastic LTS and corresponding MTBDD Boolean variable. Note that MTBDD based symbolic encodings of transition systems are also possible in the case where both Markovian and immediate ....

M. Siegle. Advances in model representation. In de Alfaro and Gilmore

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M. Siegle. Advances in Model Representations. In Proc. PAPM/PROBMIV 2001, LNCS Volume 2165, Aachen, Germany, 2001. Springer Verlag.

No context found.

Markus Siegle. Advances in Model Representations. In Luca de Alfaro and Stephen Gilmore, editors, Proc. PAPM/PROBMIV 2001, Available as Volume 2165 of LNCS, pages 1-22, Aachen, Germany, September 2001. Springer Verlag.

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Siegle, M., Advances in Model Representations, in: L. de Alfaro and S. Gilmore, editors, Proc. PAPM/PROBMIV 2001.

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