Data di Pubblicazione:
2021
Citazione:
Robust Model Checking with Imprecise Markov Reward Models / A. Termine, A. Antonucci, A. Facchini, G. Primiero (PROCEEDINGS OF MACHINE LEARNING RESEARCH). - In: International Symposium on Imprecise Probability: Theories and Applications / [a cura di] A. Cano, J. De Bock, E. Miranda, S. Moral. - Ebook. - [s.l] : PMLR, 2021. - pp. 299-309 (( Intervento presentato al 12. convegno International Symposium of Imprecise Probabilities: Theories and Applications tenutosi a Granada nel 2021.
Abstract:
In recent years probabilistic model checking has become an important area of research because of the diffusion of computational systems of stochastic nature. Despite its great success, standard probabilistic model checking suffers the limitation of requiring a sharp specification of the probabilities governing the model behaviour. The theory of imprecise probabilities offers a natural approach to overcome such limitation by a sensitivity analysis with respect to the values of these parameters. However, only extensions based on discrete-time imprecise Markov chains have been considered so far for such a robust approach to model checking. We present a further extension based on imprecise Markov reward models. In particular, we derive efficient algorithms to compute lower and upper bounds of the expected cumulative reward and probabilistic bounded rewards based on existing results for imprecise Markov chains. These ideas are tested on a real case study involving the spend-down costs of geriatric medicine departments.
Tipologia IRIS:
03 - Contributo in volume
Keywords:
Probabilistic Computational Tree Logic; Model-Checking, Imprecise Markov Chains; Imprecise Markov Reward Models
Elenco autori:
A. Termine, A. Antonucci, A. Facchini, G. Primiero
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Link al Full Text:
Titolo del libro:
International Symposium on Imprecise Probability: Theories and Applications