ARCH-COMP19 Category Report: Stochastic Modelling

Abate, Alessandro; Blom, H.A.P.; Cauchi, Nathalie; Degiorgio, Kurt; Fränzle, Martin; Hahn, Ernst Moritz; Haesaert, Sofie; Ma, Hao; Oishi, Meeko; Pilch, Carina; Remke, Anne; Salamati, Mahmoud; Soudjani, Sadegh; Huijgevoort, Birgit van; Vinod, Abraham P.

doi:10.29007/f2vb

Cited by 9 publications

(25 citation statements)

References 37 publications

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“…The Heated Tank benchmark stems from safety literature, where it is a well-known example of a Piecewise Deterministic Markov Process (PDMP) [18]. This made the Heated Tank benchmark a logical candidate for inclusion in the set of ARCH stochastic models [2,1]. The heated tank system consists of a tank containing liquid whose level is influenced by two pumps and one valve managed by a controller.…”

Section: Heated Tankmentioning

confidence: 99%

“…This makes version 4 (= 4.0) the best choice for a rare event estimation benchmark. Version 4.0 has been described in words [2], and in a formal way using formal models in Modest [33] and from SDCPN and HYPEG [1]. In [1] Heated Tank version 4.0 has been enriched with the following five extensions: 4.I.…”

Section: Heated Tankmentioning

confidence: 99%

“…The water sewage benchmark models a water sewage treatment plant situated in Enschede as a hybrid Petri net with general transitions (HPnG). It has been proposed and thoroughly analyzed in [26] and was already included in ARCH2019 [1].…”

Section: Water Sewage Facilitymentioning

confidence: 99%

“…For each extension dedicated properties have been formulated in STL. Both properties ensure that the community buffer is not exceeded and have been discussed in [1].…”

Section: Water Sewage Facilitymentioning

confidence: 99%

“…Tool development strongly profits from large sets of benchmarks for evaluation of the implemented approach. Additionally to the already existing benchmarks which have been proposed in the last years [1,2], in this section we present new benchmarks (in alphabetical order) which have been proposed this year for evaluation and introduce new challenges that are not currently handled by existing benchmarks.…”

Section: New Benchmarksmentioning

confidence: 99%

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ARCH-COMP20 Category Report: Stochastic Models

Abate¹,

Blom²,

Cauchi³

et al.

EPiC Series in Computing

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This report presents the results of a friendly competition for formal verification and policy synthesis of stochastic models. It also introduces new benchmarks within this category, and recommends next steps for this category towards next year’s edition of the competition. The friendly competition took place as part of the workshop Applied Verification for Continuous and Hybrid Systems (ARCH) in Spring/Summer 2020.

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Section: Heated Tankmentioning

confidence: 99%

Section: Heated Tankmentioning

confidence: 99%

Section: Water Sewage Facilitymentioning

confidence: 99%

“…For each extension dedicated properties have been formulated in STL. Both properties ensure that the community buffer is not exceeded and have been discussed in [1].…”

Section: Water Sewage Facilitymentioning

confidence: 99%

Section: New Benchmarksmentioning

confidence: 99%

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ARCH-COMP20 Category Report: Stochastic Models

Abate¹,

Blom²,

Cauchi³

et al.

EPiC Series in Computing

Self Cite

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AMYTISS: Parallelized Automated Controller Synthesis for Large-Scale Stochastic Systems

Lavaei

Khaled

Soudjani

2020

Lecture Notes in Computer Science

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In this paper, we propose a software tool, called AMYTISS, implemented in C++/OpenCL, for designing correct-by-construction controllers for large-scale discrete-time stochastic systems. This tool is employed to (i) build finite Markov decision processes (MDPs) as finite abstractions of given original systems, and (ii) synthesize controllers for the constructed finite MDPs satisfying bounded-time high-level properties including safety, reachability and reach-avoid specifications. In AMYTISS, scalable parallel algorithms are designed such that they support the parallel execution within CPUs, GPUs and hardware accelerators (HWAs). Unlike all existing tools for stochastic systems, AMYTISS can utilize high-performance computing (HPC) platforms and cloud-computing services to mitigate the effects of the state-explosion problem, which is always present in analyzing large-scale stochastic systems. We benchmark AMYTISS against the most recent tools in the literature using several physical case studies including robot examples, room temperature and road traffic networks. We also apply our algorithms to a 3-dimensional autonomous vehicle and 7-dimensional nonlinear model of a BMW 320i car by synthesizing an autonomous parking controller.

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On Correctness, Precision, and Performance in Quantitative Verification

Budde

Hartmanns

Klauck

et al. 2021

Lecture Notes in Computer Science

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Quantitative verification tools compute probabilities, expected rewards, or steady-state values for formal models of stochastic and timed systems. Exact results often cannot be obtained efficiently, so most tools use floating-point arithmetic in iterative algorithms that approximate the quantity of interest. Correctness is thus defined by the desired precision and determines performance. In this paper, we report on the experimental evaluation of these trade-offs performed in QComp 2020: the second friendly competition of tools for the analysis of quantitative formal models. We survey the precision guarantees-ranging from exact rational results to statistical confidence statements-offered by the nine participating tools. They gave rise to a performance evaluation using five tracks with varying correctness criteria, of which we present the results.

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ARCH-COMP19 Category Report: Stochastic Modelling

Cited by 9 publications

References 37 publications

ARCH-COMP20 Category Report: Stochastic Models

ARCH-COMP20 Category Report: Stochastic Models

AMYTISS: Parallelized Automated Controller Synthesis for Large-Scale Stochastic Systems

On Correctness, Precision, and Performance in Quantitative Verification

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