PRISM

PRISM: Probabilistic symbolic model checker. In this paper we describe PRISM, a tool being developed at the University of Birmingham for the analysis of probabilistic systems. PRISM supports three probabilistic models: discrete-time Markov chains, Markov decision processes and continuous-time Markov chains. Analysis is performed through model checking such systems against specifications written in the probabilistic temporal logics PCTL and CSL. The tool features three model checking engines: one symbolic, using BDDs (binary decision diagrams) and MTBDDs (multi-terminal BDDs); one based on sparse matrices; and one which combines both symbolic and sparse matrix methods. PRISM has been successfully used to analyse probabilistic termination, performance, and quality of service properties for a range of systems, including randomized distributed algorithms, manufacturing systems and workstation clusters.


References in zbMATH (referenced in 429 articles , 2 standard articles )

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  1. Benouhiba, Toufik: A multi-level refinement approach for structural synthesis of optimal probabilistic models (2021)
  2. Evangelidis, Alexandros; Parker, David: Quantitative verification of Kalman filters (2021)
  3. Fearnley, John; Gairing, Martin; Mnich, Matthias; Savani, Rahul: Reachability switching games (2021)
  4. Gleason, Joseph D.; Vinod, Abraham P.; Oishi, Meeko M. K.: Lagrangian approximations for stochastic reachability of a target tube (2021)
  5. Junges, Sebastian; Katoen, Joost-Pieter; Pérez, Guillermo A.; Winkler, Tobias: The complexity of reachability in parametric Markov decision processes (2021)
  6. Lanotte, Ruggero; Merro, Massimo; Tini, Simone: A probabilistic calculus of cyber-physical systems (2021)
  7. Sproston, Jeremy: Probabilistic timed automata with clock-dependent probabilities (2021)
  8. Baier, Christel; Hensel, Christian; Hutschenreiter, Lisa; Junges, Sebastian; Katoen, Joost-Pieter; Klein, Joachim: Parametric Markov chains: PCTL complexity and fraction-free Gaussian elimination (2020)
  9. Bersani, Marcello M.; Soldo, Matteo; Menghi, Claudio; Pelliccione, Patrizio; Rossi, Matteo: PuRSUE -- from specification of robotic environments to synthesis of controllers (2020)
  10. Fahrenberg, Uli; Legay, Axel; Quaas, Karin: Computing branching distances with quantitative games (2020)
  11. Fraser, Douglas; Giaquinta, Ruben; Hoffmann, Ruth; Ireland, Murray; Miller, Alice; Norman, Gethin: Collaborative models for autonomous systems controller synthesis (2020)
  12. Gainer, Paul; Linker, Sven; Dixon, Clare; Hustadt, Ullrich; Fisher, Michael: Multi-scale verification of distributed synchronisation (2020)
  13. Hartmanns, Arnd; Junges, Sebastian; Katoen, Joost-Pieter; Quatmann, Tim: Multi-cost bounded tradeoff analysis in MDP (2020)
  14. Jamroga, Wojciech; Konikowska, Beata; Kurpiewski, Damian; Penczek, Wojciech: Multi-valued verification of strategic ability (2020)
  15. Křetínský, Jan; Meggendorfer, Tobias: Of cores: a partial-exploration framework for Markov decision processes (2020)
  16. Lavaei, Abolfazl; Khaled, Mahmoud; Soudjani, Sadegh; Zamani, Majid: AMYTISS: a parallelized tool on automated controller synthesis for large-scale stochastic systems (2020)
  17. Mathur, Umang; Bauer, Matthew S.; Chadha, Rohit; Sistla, A. Prasad; Viswanathan, Mahesh: Exact quantitative probabilistic model checking through rational search (2020)
  18. Michaliszyn, Jakub; Otop, Jan: Non-deterministic weighted automata evaluated over Markov chains (2020)
  19. Tang, Qiyi; van Breugel, Franck: Deciding probabilistic bisimilarity distance one for probabilistic automata (2020)
  20. Thiagarajan, P. S.; Yang, Shaofa: A theory of distributed Markov chains (2020)

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Further publications can be found at: http://www.prismmodelchecker.org/publications.php