Assume-Guarantee Abstraction Refinement for Probabilistic Systems

Komuravelli, Anvesh; Păsăreanu, Corina S.; Clarke, Edmund M.

doi:10.1007/978-3-642-31424-7_25

Cited by 39 publications

(55 citation statements)

References 17 publications

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“…The main difference w.r.t. [65] is that strong simulation preserves exact probabilities and therefore the algorithm of [65] requires numerical algorithms whereas our algorithm requires only discrete graph algorithms. Moreover, our approach can be applied uniformly both to MDPs and two-player games.…”

Section: Related Workmentioning

confidence: 99%

“…[65]) in our tool actions are observable instead of atomic propositions. Our algorithms are easily adapted to this setting.…”

Section: Observable Actionsmentioning

confidence: 99%

“…The CEGAR approach has been adapted to probabilistic systems for reachability [60] and safe-pCTL [13] under monolithic (non-compositional) abstraction refinement. The work of [65] considers CEGAR for compositional analysis of probabilistic system with strong simulation. The main difference w.r.t.…”

Section: Related Workmentioning

confidence: 99%

“…We used our tool on all the MDP examples and specifications from [65]. The specifications describe safety properties of the systems.…”

Section: Mdp Examples With Safety Specificationsmentioning

confidence: 99%

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CEGAR for compositional analysis of qualitative properties in Markov decision processes

Chatterjee

Chmelík

Daca

2015

Form Methods Syst Des

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We consider Markov decision processes (MDPs) which are a standard model for probabilistic systems. We focus on qualitative properties for MDPs that can express that desired behaviors of the system arise almost-surely (with probability 1) or with positive probability. We introduce a new simulation relation to capture the refinement relation of MDPs with respect to qualitative properties, and present discrete graph algorithms with quadratic complexity to compute the simulation relation. We present an automated technique for assume-guarantee style reasoning for compositional analysis of two-player games by giving a counterexample guided abstraction-refinement approach to compute our new simulation relation. We show a tight link between two-player games and MDPs, and as a consequence the results for games are lifted to MDPs with qualitative properties. We have implemented our algorithms and show that the compositional analysis leads to significant improvements.

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Section: Related Workmentioning

confidence: 99%

“…[65]) in our tool actions are observable instead of atomic propositions. Our algorithms are easily adapted to this setting.…”

Section: Observable Actionsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…We used our tool on all the MDP examples and specifications from [65]. The specifications describe safety properties of the systems.…”

Section: Mdp Examples With Safety Specificationsmentioning

confidence: 99%

See 2 more Smart Citations

CEGAR for compositional analysis of qualitative properties in Markov decision processes

Chatterjee

Chmelík

Daca

2015

Form Methods Syst Des

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“…There is also work on learning-based assume-guarantee reasoning for synchronous probabilistic systems [5], assume-guarantee and abstraction refinement for probabilistic systems [9], and on compositional reasoning for probabilistic model checking of hardware designs [10]. Our approach is also compositional, but does not involve assume-guarantee reasoning.…”

Section: Related Workmentioning

confidence: 99%

Probabilistic Verification of Coordinated Multi-robot Missions

Chaki

Giampapa

2013

Model Checking Software

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Abstract. Robots are increasingly used to perform a wide variety of tasks, especially those involving dangerous or inaccessible locations. As the complexity of such tasks grow, robots are being deployed in teams, with complex coordination schemes aimed at maximizing the chance of mission success. Such teams operate under inherently uncertain conditions -the robots themselves fail, and have to continuously adapt to changing environmental conditions. A key challenge facing robotic mission designers is therefore to construct a mission -i.e., specify number and type of robots, number and size of teams, coordination and planning mechanisms etc. -so as to maximize some overall utility, such as the probability of mission success. In this paper, we advocate, formalize, and empirically justify an approach to compute quantitative utility of robotic missions using probabilistic model checking. We show how to express a robotic demining mission as a restricted type of discrete time Markov chain (called αPA), and its utility as either a linear temporal logic formula or a reward. We prove a set of compositionality theorems that enable us to compute the utility of a system composed of several αPAs by combining the utilities of each αPA in isolation. This ameliorates the statespace explosion problem, even when the system being verified is composed of a large number of robots. We validate our approach empirically, using the probabilistic model checker prism.

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PBlaman: performance blame analysis based on Palladio contracts

Brüseke

Wachsmuth

Engels

et al. 2014

Concurrency and Computation

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SUMMARYIn performance-driven software engineering, the performance of a system is evaluated through models before the system is assembled. After assembly, the performance is then validated using performance tests. When a component-based system fails certain performance requirements during the tests, it is important to find out whether individual components yield performance errors or whether the composition of components is faulty. This task is called performance blame analysis. Existing performance blame analysis approaches and also alternative error analysis approaches are restricted, because they either do not employ expected values, use expected values from regression testing, or use static developer-set limits. In contrast, this paper describes the new performance blame analysis approach PBlaman that builds upon our previous work and that employs the context-portable performance contracts of Palladio. PBlaman decides what components to blame by comparing the observed response time data series of each single component operation in a failed test case to the operation's expected response time data series derived from the contracts. System architects are then assisted by a visual presentation of the obtained analysis results. We exemplify the benefits of PBlaman in two case studies, each of which representing applications that follow a particular architectural style.

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Assume-Guarantee Abstraction Refinement for Probabilistic Systems

Cited by 39 publications

References 17 publications

CEGAR for compositional analysis of qualitative properties in Markov decision processes

CEGAR for compositional analysis of qualitative properties in Markov decision processes

Probabilistic Verification of Coordinated Multi-robot Missions

PBlaman: performance blame analysis based on Palladio contracts

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