Probabilistic Hyperproperties of Markov Decision Processes

Dimitrova, Rayna; Finkbeiner, Bernd; Torfah, Hazem

doi:10.1007/978-3-030-59152-6_27

Cited by 23 publications

(16 citation statements)

References 20 publications

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“…While hyperproperties are useful to specifying system properties, it was observed in [33] that they can also be used to formalize causality. Similar observations have been made for probabilistic causation [2,37].…”

Section: Further Approaches Inspired By Hp Causalitysupporting

confidence: 80%

“…While the techniques presented here are limited to purely probabilistic models (Markov chains), an examination of causality in probabilistic models with nondeterminism (Markov decision processes) is largely open. A first step in this direction is a formalization of action causes as a hyperproperty in Markov decision processes [37]. Another important future direction is to reason about cause-effect relationships in hidden Markovian models where states (and events) are not fully observable.…”

Section: Discussionmentioning

confidence: 99%

“…In terms of formal models, Pearl's early notion of actual causality in terms of causal beams [96,97] entailed probabilistic flavor, and the causal probabilistic logic of [115,14] describes a language for reasoning about probabilistic causation. Nonetheless, we are aware of only a few works that study a probabilistic version of causality in operational models (see, e.g., [75,2,37,9]). Along these lines, we point out open directions for research that focus on the operational point of view.…”

Section: :3mentioning

confidence: 99%

See 2 more Smart Citations

From Verification to Causality-Based Explications (Invited Talk)

Baier¹,

Dubslaff²,

Funke³

et al. 2021

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In view of the growing complexity of modern software architectures, formal models are increasingly used to understand why a system works the way it does, opposed to simply verifying that it behaves as intended. This paper surveys approaches to formally explicate the observable behavior of reactive systems. We describe how Halpern and Pearl's notion of actual causation inspired verificationoriented studies of cause-effect relationships in the evolution of a system. A second focus lies on applications of the Shapley value to responsibility ascriptions, aimed to measure the influence of an event on an observable effect. Finally, formal approaches to probabilistic causation are collected and connected, and their relevance to the understanding of probabilistic systems is discussed.

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Section: Further Approaches Inspired By Hp Causalitysupporting

confidence: 80%

Section: Discussionmentioning

confidence: 99%

Section: :3mentioning

confidence: 99%

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From Verification to Causality-Based Explications (Invited Talk)

Baier¹,

Dubslaff²,

Funke³

et al. 2021

View full text Add to dashboard Cite

show abstract

“…To the best of our knowledge, probabilistic causation in MDPs has not been studied before. The only work in this direction we are aware of is the recent paper by Dimitrova et al [16] on a hyperlogic, called PHL, for MDPs. While the paper focuses on the foundation of PHL, it contains an example illustrating how action causality can be formalized as a PHL formula.…”

Section: Related Workmentioning

confidence: 99%

On probability-raising causality in Markov decision processes

Baier¹,

Funke²,

Piribauer³

et al. 2022

Preprint

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The purpose of this paper is to introduce a notion of causality in Markov decision processes based on the probability-raising principle and to analyze its algorithmic properties. The latter includes algorithms for checking causeeffect relationships and the existence of probability-raising causes for given effect scenarios. Inspired by concepts of statistical analysis, we study quality measures (recall, coverage ratio and f-score) for causes and develop algorithms for their computation. Finally, the computational complexity for finding optimal causes with respect to these measures is analyzed.

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“…In recent years, the linear-time and branching-time temporal logics have been extended to allow for the specification of hyperproperties [3,11,8,5,7]. Hyperproperties are a generalization of trace properties.…”

Section: Introductionmentioning

confidence: 99%

Model Checking Algorithms for Hyperproperties (Invited Paper)

Finkbeiner

2021

Lecture Notes in Computer Science

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Hyperproperties generalize trace properties by expressing relations between multiple computations. Hyperpropertes include policies from information-flow security, like observational determinism or noninterference, and many other system properties including promptness and knowledge. In this paper, we give an overview on the model checking problem for temporal hyperlogics. Our starting point is the model checking algorithm for HyperLTL, a reduction to Büchi automata emptiness. This basic construction can be extended with propositional quantification, resulting in an algorithm for HyperQPTL. It can also be extended with branching time, resulting in an algorithm for HyperCTL * . However, it is not possible to have both extensions at the same time: the model checking problem of HyperQCTL * is undecidable. An attractive compromise is offered by MPL[E], i.e., monadic path logic extended with the equal-level predicate. The expressiveness of MPL[E] falls strictly between that of HyperCTL * and HyperQCTL * . MPL[E] subsumes both HyperCTL * and HyperKCTL * , the extension of HyperCTL * with the knowledge operator. We show that the model checking problem for MPL[E] is still decidable.

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Probabilistic Hyperproperties of Markov Decision Processes

Cited by 23 publications

References 20 publications

From Verification to Causality-Based Explications (Invited Talk)

From Verification to Causality-Based Explications (Invited Talk)

On probability-raising causality in Markov decision processes

Model Checking Algorithms for Hyperproperties (Invited Paper)

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