HyperPCTL: A Temporal Logic for Probabilistic Hyperproperties
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Abstract (arXiv)
In this paper, we propose a new logic for expressing and reasoning about probabilistic hyperproperties. Hyperproperties characterize the relation between different independent executions of a system. Probabilistic hyperproperties express quantitative dependencies between such executions. The standard temporal logics for probabilistic systems, i.e., PCTL and PCTL* can refer only to a single path at a time and, hence, cannot express many probabilistic hyperproperties of interest. The logic proposed in this paper, HyperPCTL, adds explicit and simultaneous quantification over multiple traces to PCTL. Such quantification allows expressing probabilistic hyperproperties. A model checking algorithm for the proposed logic is also given for discrete-time Markov chains.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 5 Apr 2018.
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Citing papers per year: 2023: 2; 2024: 0; 2025: 0; 2026: 1.
3 papers.
- 2023Counterfactuals Modulo Temporal Logicscited by 1
- 2023Deductive Controller Synthesis for Probabilistic Hyperpropertiescited by 1
- 2026Property-driven Causal Abstractions for Markov Decision Processescited by 0
- 2026Property-driven Causal Abstractions for Markov Decision Processes29 Jul 2026
- 2023Deductive Controller Synthesis for Probabilistic Hyperproperties10 Jul 2023
- 2023Counterfactuals Modulo Temporal Logics15 Jun 2023