Exploiting process lifetime distributions for dynamic load balancing
ACM Transactions on Computer Systems (TOCS)
Learning DFA from Simple Examples
Machine Learning
Continuous-time hidden Markov models for network performance evaluation
Performance Evaluation
Stochastic Grammatical Inference with Multinomial Tests
ICGI '02 Proceedings of the 6th International Colloquium on Grammatical Inference: Algorithms and Applications
Learning Continuous Time Markov Chains from Sample Executions
QEST '04 Proceedings of the The Quantitative Evaluation of Systems, First International Conference
Verification and planning for stochastic processes with asynchronous events
Verification and planning for stochastic processes with asynchronous events
Scheduling: Theory, Algorithms, and Systems
Scheduling: Theory, Algorithms, and Systems
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Statistical model checking: an overview
RV'10 Proceedings of the First international conference on Runtime verification
Statistical verification of probabilistic properties with unbounded until
SBMF'10 Proceedings of the 13th Brazilian conference on Formal methods: foundations and applications
Time for statistical model checking of real-time systems
CAV'11 Proceedings of the 23rd international conference on Computer aided verification
libalf: the automata learning framework
CAV'10 Proceedings of the 22nd international conference on Computer Aided Verification
Ymer: a statistical model checker
CAV'05 Proceedings of the 17th international conference on Computer Aided Verification
Learning techniques for software verification and validation
ISoLA'12 Proceedings of the 5th international conference on Leveraging Applications of Formal Methods, Verification and Validation: technologies for mastering change - Volume Part I
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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.