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 416 articles , 2 standard articles )

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  1. Baier, Christel; Hensel, Christian; Hutschenreiter, Lisa; Junges, Sebastian; Katoen, Joost-Pieter; Klein, Joachim: Parametric Markov chains: PCTL complexity and fraction-free Gaussian elimination (2020)
  2. Bersani, Marcello M.; Soldo, Matteo; Menghi, Claudio; Pelliccione, Patrizio; Rossi, Matteo: PuRSUE -- from specification of robotic environments to synthesis of controllers (2020)
  3. Fahrenberg, Uli; Legay, Axel; Quaas, Karin: Computing branching distances with quantitative games (2020)
  4. Fraser, Douglas; Giaquinta, Ruben; Hoffmann, Ruth; Ireland, Murray; Miller, Alice; Norman, Gethin: Collaborative models for autonomous systems controller synthesis (2020)
  5. Gainer, Paul; Linker, Sven; Dixon, Clare; Hustadt, Ullrich; Fisher, Michael: Multi-scale verification of distributed synchronisation (2020)
  6. Hartmanns, Arnd; Junges, Sebastian; Katoen, Joost-Pieter; Quatmann, Tim: Multi-cost bounded tradeoff analysis in MDP (2020)
  7. Křetínský, Jan; Meggendorfer, Tobias: Of cores: a partial-exploration framework for Markov decision processes (2020)
  8. Lavaei, Abolfazl; Khaled, Mahmoud; Soudjani, Sadegh; Zamani, Majid: AMYTISS: a parallelized tool on automated controller synthesis for large-scale stochastic systems (2020)
  9. Mathur, Umang; Bauer, Matthew S.; Chadha, Rohit; Sistla, A. Prasad; Viswanathan, Mahesh: Exact quantitative probabilistic model checking through rational search (2020)
  10. Michaliszyn, Jakub; Otop, Jan: Non-deterministic weighted automata evaluated over Markov chains (2020)
  11. Tang, Qiyi; van Breugel, Franck: Deciding probabilistic bisimilarity distance one for probabilistic automata (2020)
  12. Aichernig, Bernhard K.; Tappler, Martin: Probabilistic black-box reachability checking (extended version) (2019)
  13. Bartocci, Ezio; Kovács, Laura; Stankovič, Miroslav: Automatic generation of moment-based invariants for prob-solvable loops (2019)
  14. Bozzano, Marco; Cimatti, Alessandro; Mattarei, Cristian: Formal reliability analysis of redundancy architectures (2019)
  15. Camacho, Carlos; Llana, Luis; Núñez, Alberto; Bravetti, Mario: Probabilistic software product lines (2019)
  16. Cardelli, Luca; Tribastone, Mirco; Tschaikowski, Max; Vandin, Andrea: Symbolic computation of differential equivalences (2019)
  17. Cauchi, Nathalie; Abate, Alessandro: Poster abstract: StocHy -- automated verification and synthesis of stochastic processes. (2019)
  18. Chatterjee, Krishnendu; Henzinger, Thomas A.; Otop, Jan: Quantitative automata under probabilistic semantics (2019)
  19. Gutierrez, Julian; Harrenstein, Paul; Perelli, Giuseppe; Wooldridge, Michael: Nash equilibrium and bisimulation invariance (2019)
  20. Jamroga, Wojciech; Malvone, Vadim; Murano, Aniello: Natural strategic ability (2019)

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