Learning stochastic timed automata from sample executions
; Melo de Sousa, -
Learning stochastic timed automata from sample executions, Proc International Symp. On Leveraging Applications of Formal Methods, Verification and Validation - ISOLA, heraklion, Greece, Vol. 10, pp. 1 - 15, October, 2012.
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Generalized semi-Markov processes are an important class of stochastic systems which are generated by stochastic timed automata. In this paper we present a novel methodology to learn this type of stochastic timed automata from sample executions of a stochastic discrete event system. Apart from its theoretical interest for machine learning area, our algorithm can be used for quantitative analysis and verification in the context of model checking. We demonstrate that the proposed learning algorithm, in the limit, correctly identifies the generalized semi-Markov process given a structurally complete sample. This paper also presents a Matlab toolbox for our algorithm and a case study of the analysis for a multi-processor system scheduler with uncertainty in task duration