Smart sampling for lightweight verification of Markov decision processes
Markov decision processes (MDP) are useful to model optimisation problems in concurrent systems. To verify MDPs with efficient Monte Carlo techniques requires that their nondeterminism be resolved by a scheduler. Recent work has introduced the elements of lightweight techniques to sample directly fr...
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| Autores principales: | , , , |
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| Formato: | article |
| Lenguaje: | Inglés |
| Publicado: |
2022
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| Materias: | |
| Acceso en línea: | http://hdl.handle.net/11086/27275 https://doi.org/10.48550/arXiv.1409.2116 |
| Aporte de: |
| Sumario: | Markov decision processes (MDP) are useful to model optimisation problems in concurrent systems. To verify MDPs with efficient Monte Carlo techniques requires that their nondeterminism be resolved by a scheduler. Recent work has introduced the elements of lightweight techniques to sample directly from scheduler space, but finding optimal schedulers by simple sampling may be inefficient. Here we describe “smart” sampling algorithms that can make substantial improvements in performance. |
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