Polynomial-Time Verification of PCTL Properties of MDPs with Convex Uncertainties

Puggelli, Alberto; Li, Wenchao; Sangiovanni-Vincentelli, A.; Seshia, Sanjit A.

doi:10.1007/978-3-642-39799-8_35

Cited by 52 publications

(22 citation statements)

References 31 publications

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“…Recently, several techniques have been developed [Chatterjee et al 2008;Benedikt et al 2013;Chen et al 2013;Puggelli et al 2013]. Typically, these methods work by determining the extremal values a probabilistic parameter can take.…”

Section: Dealing With Uncertainty In Control Strategies For Adaptationmentioning

confidence: 99%

Control Strategies for Self-Adaptive Software Systems

Filieri

Maggio

Angelopoulos³

et al. 2017

ACM Trans. Auton. Adapt. Syst.

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The pervasiveness and growing complexity of software systems is challenging software engineering to design systems that can adapt their behavior to withstand unpredictable, uncertain, and continuously changing execution environments. Control theoretical adaptation mechanisms received a growing interest from the software engineering community in the last years for their mathematical grounding allowing formal guarantees on the behavior of the controlled systems. However, most of these mechanisms are tailored to specific applications and can hardly be generalized into broadly applicable software design and development processes.This paper discusses a reference control design process, from goal identification to the verification and validation of the controlled system. A taxonomy of the main control strategies is introduced, analyzing their applicability to software adaptation for both functional and non-functional goals. A brief extract on how to deal with uncertainty complements the discussion. Finally, the paper highlights a set of open challenges, both for the software engineering and the control theory research communities.

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Section: Dealing With Uncertainty In Control Strategies For Adaptationmentioning

confidence: 99%

Control Strategies for Self-Adaptive Software Systems

Filieri

Maggio

Angelopoulos³

et al. 2017

ACM Trans. Auton. Adapt. Syst.

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“…The once-and-for-all IMC semantics ( [7,19,16]) is alike to the semantics for pMC, as introduced above. The associated satisfaction relation |= o I is defined as follows:…”

Section: Existing MC Abstraction Modelsmentioning

confidence: 99%

Reachability in Parametric Interval Markov Chains Using Constraints

Bart

Delahaye

Lime

et al. 2017

Quantitative Evaluation of Systems

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Abstract. Parametric Interval Markov Chains (pIMCs) are a specification formalism that extend Markov Chains (MCs) and Interval Markov Chains (IMCs) by taking into account imprecision in the transition probability values: transitions in pIMCs are labeled with parametric intervals of probabilities. In this work, we study the difference between pIMCs and other Markov Chain abstractions models and investigate the two usual semantics for IMCs: once-and-for-all and at-every-step. In particular, we prove that both semantics agree on the maximal/minimal reachability probabilities of a given IMC. We then investigate solutions to several parameter synthesis problems in the context of pIMCs -consistency, qualitative reachability and quantitative reachability -that rely on constraint encodings. Finally, we propose a prototype implementation of our constraint encodings with promising results.

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“…However, they mostly rely on ordering between intervals which is not needed in our case. Furthermore, Puggelli et al [26] proposed polynomial algorithms for Markov chains with uncertainty based on optimization techniques, but only for reachability and PCTL properties.…”

Section: Related Workmentioning

confidence: 99%