Probabilistic self-stabilization
Information Processing Letters
The power of amnesia: learning probabilistic automata with variable memory length
Machine Learning - Special issue on COLT '94
Learning Stochastic Regular Grammars by Means of a State Merging Method
ICGI '94 Proceedings of the Second International Colloquium on Grammatical Inference and Applications
Probabilistic Verification of Discrete Event Systems Using Acceptance Sampling
CAV '02 Proceedings of the 14th International Conference on Computer Aided Verification
Learning Continuous Time Markov Chains from Sample Executions
QEST '04 Proceedings of the The Quantitative Evaluation of Systems, First International Conference
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Principles of Model Checking (Representation and Mind Series)
Principles of Model Checking (Representation and Mind Series)
PRISM 4.0: verification of probabilistic real-time systems
CAV'11 Proceedings of the 23rd international conference on Computer aided verification
Learning Probabilistic Automata for Model Checking
QEST '11 Proceedings of the 2011 Eighth International Conference on Quantitative Evaluation of SysTems
Context tree estimation for not necessarily finite memory processes, via BIC and MDL
IEEE Transactions on Information Theory
| Hi-index | 0.00 |
Establishing an accurate model for formal verification of an existing hardware or software system is often a manual process that is both time consuming and resource demanding. In order to ease the model construction phase, methods have recently been proposed for automatically learning accurate system models from data in the form of observations of the target system. Common for these approaches is that they assume the data to consist of multiple independent observation sequences. However, for certain types of systems, in particular many running embedded systems, one would only have access to a single long observation sequence, and in these situations existing automatic learning methods cannot be applied. In this paper, we adapt algorithms for learning variable order Markov chains from a single observation sequence of a target system, so that stationary system properties can be verified using the learned model. Experiments demonstrate that system properties (formulated as stationary probabilities of LTL formulas) can be reliably identified using the learned model.