The probabilistic model checker Storm

Hensel, Christian; Junges, Sebastian; Katoen, Joost-Pieter; Quatmann, Tim; Volk, Matthias

doi:10.1007/s10009-021-00633-z

Cited by 104 publications

(95 citation statements)

References 109 publications

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“…Implementation details. We integrated Algorithm 1 in the probabilistic model checker Storm [22] as an extension of the POMDP verification framework described in [8]. Inputs are a POMDP-encoded either explicitly or using an extension of the Prism language [36]-and a property specification.…”

Section: Experimental Evaluationmentioning

confidence: 99%

“…Experimental results. We have implemented our cut-off and belief clipping approaches on top of the probabilistic model checker Storm [22] and applied it to a range of various benchmarks. We provide a comparison with the model checking approach in [36], and determine the tightness of our under-approximations by comparing them to over-approximations obtained using the algorithm from [8].…”

Section: Introductionmentioning

confidence: 99%

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Under-Approximating Expected Total Rewards in POMDPs

Bork¹,

Katoen²,

Quatmann³

2022

Preprint

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We consider the problem: is the optimal expected total reward to reach a goal state in a partially observable Markov decision process (POMDP) below a given threshold? We tackle this-generally undecidable-problem by computing under-approximations on these total expected rewards. This is done by abstracting finite unfoldings of the infinite belief MDP of the POMDP. The key issue is to find a suitable under-approximation of the value function. We provide two techniques: a simple (cut-off) technique that uses a good policy on the POMDP, and a more advanced technique (belief clipping) that uses minimal shifts of probabilities between beliefs. We use mixed-integer linear programming (MILP) to find such minimal probability shifts and experimentally show that our techniques scale quite well while providing tight lower bounds on the expected total reward.

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Section: Experimental Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Under-Approximating Expected Total Rewards in POMDPs

Bork¹,

Katoen²,

Quatmann³

2022

Preprint

Self Cite

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“…It is written in Python and is well integrated with interactive Jupyter notebooks [19] for visualization of CMDPs and algorithms. With STORM [1] and STORMPY installed, FIMDP can read models in PRISM [20] or JANI [21] languages.…”

Section: A Tools Examples and Evaluation Settingmentioning

confidence: 99%

“…In particular, the goal-leaning and threshold heuristics attempt, as a secondary objective, to reach T in a short time. Further, we briefly describe our tool implementing these algorithms and we demonstrate that our approach specialized to qualitative analysis of resource-constrained systems can solve this task faster then the state-of-the-art generalpurpose probabilistic model checker STORM [1].…”

Section: Introductionmentioning

confidence: 99%

Efficient Strategy Synthesis for MDPs with Resource Constraints

Blahoudek¹,

Novotný²,

Ornik³

et al. 2021

Preprint

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We consider qualitative strategy synthesis for the formalism called consumption Markov decision processes. This formalism can model dynamics of an agents that operates under resource constraints in a stochastic environment. The presented algorithms work in time polynomial with respect to the representation of the model and they synthesize strategies ensuring that a given set of goal states will be reached (once or infinitely many times) with probability 1 without resource exhaustion. In particular, when the amount of resource becomes too low to safely continue in the mission, the strategy changes course of the agent towards one of a designated set of reload states where the agent replenishes the resource to full capacity; with sufficient amount of resource, the agent attempts to fulfill the mission again.We also present two heuristics that attempt to reduce expected time that the agent needs to fulfill the given mission, a parameter important in practical planning. The presented algorithms were implemented and numerical examples demonstrate (i) the effectiveness (in terms of computation time) of the planning approach based on consumption Markov decision processes and (ii) the positive impact of the two heuristics on planning in a realistic example.

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“…Implementation. We implemented the BDD translation for static fault trees (SFTs) in the Storm model checker [31] and use the multi-core BDD library Sylvan [19]. Our implementation Storm-dft supports computing minimal cut sets (MCS), the unreliability and several importance measures such as the Birnbaum index [7].…”

Section: Introductionmentioning

confidence: 99%

Artifact for "BDDs Strike Back - Efficient Analysis of Static and Dynamic Fault Trees"

Basgöze,

Volk,

Katoen

et al. 2022

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Fault trees are a key model in reliability analysis. Classical static fault trees (SFT) can best be analysed using binary decision diagrams (BDD). State-based techniques are favorable for the more expressive dynamic fault trees (DFT). This paper combines the best of both worlds by following Dugan's approach: dynamic sub-trees are analysed via model checking Markov models and replaced by basic events capturing the obtained failure probabilities. The resulting SFT is then analysed via BDDs. We implemented this approach in the Storm model checker. Extensive experiments (a) compare our pure BDD-based analysis of SFTs to various existing SFT analysis tools, (b) indicate the benefits of our efficient calculations for multiple time points and the assessment of the mean-time-to-failure, and (c) show that our implementation of Dugan's approach significantly outperforms pure Markovian analysis of DFTs. Our implementation Storm-dft is currently the only tool supporting efficient analysis for both SFTs and DFTs. This work has been partially funded by NWO under the grant PrimaVera number NWA.1160.18.238. Khan is funded by a HEC-DAAD stipend.

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The probabilistic model checker Storm

Cited by 104 publications

References 109 publications

Under-Approximating Expected Total Rewards in POMDPs

Under-Approximating Expected Total Rewards in POMDPs

Efficient Strategy Synthesis for MDPs with Resource Constraints

Artifact for "BDDs Strike Back - Efficient Analysis of Static and Dynamic Fault Trees"

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