Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-34026-0_38 | ISoLA (1) |
Keywords | Field | DocType |
quantitative analysis,complete sample,stochastic discrete event system,generalized semi-markov process,important class,case study,sample execution,multi-processor system scheduler,stochastic system,matlab toolbox | Word clock,Discrete event system,Model checking,Computer science,Matlab toolbox,Automaton,Theoretical computer science | Conference |
Citations | PageRank | References |
3 | 0.42 | 17 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| André de Matos Pedro | 1 | 6 | 2.21 |
| Paul Andrew Crocker | 2 | 3 | 0.42 |
| Simão Melo de Sousa | 3 | 95 | 9.60 |