Synthesis of Optimal Resilient Control Strategies

Baier, Christel; Dubslaff, Clemens; Korenčiak, Ľuboš; Kučera, Antonín; Řehák, Vojtěch

doi:10.1007/978-3-319-68167-2_27

Cited by 7 publications

(3 citation statements)

References 15 publications

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“…[35,41] address multi-objective quantiles on reachability properties while [50,20] consider multi-objective combinations of percentile queries on MDP and LRA objectives. [6] treats resilient systems ensuring constraints on the repair mechanism while maximizing the expected LRA reward when being operational. The trade-off between expected LRA rewards and their variance is analyzed in [13].…”

Section: Other Related Work Mixtures Of Various Other Objectives Havementioning

confidence: 99%

Multi-objective Optimization of Long-run Average and Total Rewards

Quatmann

Katoen

2021

Tools and Algorithms for the Construction and Analysis of Systems

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This paper presents an efficient procedure for multi-objective model checking of long-run average reward (aka: mean pay-off) and total reward objectives as well as their combination. We consider this for Markov automata, a compositional model that captures both traditional Markov decision processes (MDPs) as well as a continuous-time variant thereof. The crux of our procedure is a generalization of Forejt et al.’s approach for total rewards on MDPs to arbitrary combinations of long-run and total reward objectives on Markov automata. Experiments with a prototypical implementation on top of the Storm model checker show encouraging results for both model types and indicate a substantial improved performance over existing multi-objective long-run MDP model checking based on linear programming.

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Section: Other Related Work Mixtures Of Various Other Objectives Havementioning

confidence: 99%

Multi-objective Optimization of Long-run Average and Total Rewards

Quatmann

Katoen

2021

Tools and Algorithms for the Construction and Analysis of Systems

View full text Add to dashboard Cite

show abstract

“…One prevalent workaround is to define multiple reward structures, where states are assigned tuples of real numbers depicting how favorable they are with respect to multiple criteria. The synthesis problem is then reduced to an optimization problem over either a normalized version of the rewards (i.e., assigning weights), or one reward with logical constraints on the others [1,7]. Results are typically presented as Pareto curves, depicting feasible points in the reward space [14].…”

Section: Related Workmentioning

confidence: 99%

Context-Aware Temporal Logic for Probabilistic Systems

Elfar¹,

Wang²,

Pajić³

2020

Preprint

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In this paper, we introduce the context-aware probabilistic temporal logic (CAPTL) that provides an intuitive way to formalize system requirements by a set of PCTL objectives with a context-based priority structure. We formally present the syntax and semantics of CAPTL and propose a synthesis algorithm for CAPTL requirements. We also implement the algorithm based on the PRISM-games model checker. Finally, we demonstrate the usage of CAPTL on two case studies: a robotic task planning problem, and synthesizing error-resilient scheduler for micro-electrode-dot-array digital microfluidic biochips.

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Section: Introductionmentioning

confidence: 99%

Multi-objective Optimization of Long-run Average and Total Rewards

Quatmann¹,

Katoen²

2020

Preprint

View full text Add to dashboard Cite

This paper presents an efficient procedure for multi-objective model checking of long-run average reward (aka: mean pay-off) and total reward objectives as well as their combination. We consider this for Markov automata, a compositional model that captures both traditional Markov decision processes (MDPs) as well as a continuous-time variant thereof. The crux of our procedure is a generalization of Forejt et al.'s approach for total rewards on MDPs to arbitrary combinations of longrun and total reward objectives on Markov automata. Experiments with a prototypical implementation on top of the Storm model checker show encouraging results for both model types and indicate a substantial improved performance over existing multi-objective long-run MDP model checking based on linear programming.

show abstract

Synthesis of Optimal Resilient Control Strategies

Cited by 7 publications

References 15 publications

Multi-objective Optimization of Long-run Average and Total Rewards

Multi-objective Optimization of Long-run Average and Total Rewards

Context-Aware Temporal Logic for Probabilistic Systems

Multi-objective Optimization of Long-run Average and Total Rewards

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