Energy-Utility Quantiles

Baier, Christel; Daum, Marcus; Dubslaff, Clemens; Klein, Joachim; Klüppelholz, Sascha

doi:10.1007/978-3-319-06200-6_24

Cited by 43 publications

(52 citation statements)

References 20 publications

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“…Similar to cost-bounded reachability queries, we compute cost-bounded expected costs via computing (weighted) expected costs within epoch models. The computation of quantiles for single-cost bounded reachability has been discussed in [3,34], where multiple cost bounds are supported via unfolding. Unfolding requires to fix bound values b 2 , .…”

Section: Expected Cost Objectivesmentioning

confidence: 99%

Multi-cost Bounded Reachability in MDP

Hartmanns

Junges

Katoen

et al. 2018

Tools and Algorithms for the Construction and Analysis of Systems

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Abstract. We provide an efficient algorithm for multi-objective modelchecking problems on Markov decision processes (MDPs) with multiple cost structures. The key problem at hand is to check whether there exists a scheduler for a given MDP such that all objectives over cost vectors are fulfilled. Reachability and expected cost objectives are covered and can be mixed. Empirical evaluation shows the algorithm's scalability. We discuss the need for output beyond Pareto curves and exploit the available information from the algorithm to support decision makers.

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Section: Expected Cost Objectivesmentioning

confidence: 99%

Multi-cost Bounded Reachability in MDP

Hartmanns

Junges

Katoen

et al. 2018

Tools and Algorithms for the Construction and Analysis of Systems

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“…The first one is a reformulation of the value iteration-based variant [14] of the algorithm introduced in [2]. We incorporate a simple fix for the problem that the error accumulation over the sequence of value iterations had not been accounted for and refer to the result as algorithm modvi.…”

Section: Reward-bounded Analysis Techniquesmentioning

confidence: 99%

“…We recall the technique for model-checking reward-bounded properties of [ 2] that avoids unfolding. It was originally formulated as a sequence of linear programming (LP) problems LP i , each corresponding to bound i ≤ b.…”

Section: Sequential Value Iterationsmentioning

confidence: 99%

“…up to b, storing the current layer and the last r result vectors only. This process can be implemented by solving a sequence of b linear programming (LP) problems no larger than the original unbounded model [2]. While it solves the memory problem in principle, LP is known not to scale to large MDP in practice.…”

Section: Introductionmentioning

confidence: 99%

“…This has been exploited to obtain quantiles [2], and we use it more generally to compute the entire cumulative (sub)distribution function (cdf for short) over the bound up to b at no extra cost. We have implemented all techniques in the mcsta tool (Sect.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Comparison of Time- and Reward-Bounded Probabilistic Model Checking Techniques

Hahn

Hartmanns

2016

Dependable Software Engineering: Theories, Tools, and Applications

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In the design of probabilistic timed systems, requirements concerning behaviour that occurs within a given time or energy budget are of central importance. We observe that model-checking such requirements for probabilistic timed automata can be reduced to checking reward-bounded properties on Markov decision processes. This is traditionally implemented by unfolding the model according to the bound, or by solving a sequence of linear programs. Neither scales well to large models. Using value iteration in place of linear programming achieves scalability but accumulates approximation error. In this paper, we correct the value iteration-based scheme, present two new approaches based on scheduler enumeration and state elimination, and compare the practical performance and scalability of all techniques on a number of case studies from the literature. We show that state elimination can significantly reduce runtime for large models or high bounds.

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