Theoretical Computer Science
Journal of Automata, Languages and Combinatorics
Introduction to the Theory of Computation
Introduction to the Theory of Computation
ICGI '98 Proceedings of the 4th International Colloquium on Grammatical Inference
Stochastic Grammatical Inference with Multinomial Tests
ICGI '02 Proceedings of the 6th International Colloquium on Grammatical Inference: Algorithms and Applications
Learning Stochastic Regular Grammars by Means of a State Merging Method
ICGI '94 Proceedings of the Second International Colloquium on Grammatical Inference and Applications
PAC-learnability of Probabilistic Deterministic Finite State Automata
The Journal of Machine Learning Research
One-Clock Deterministic Timed Automata Are Efficiently Identifiable in the Limit
LATA '09 Proceedings of the 3rd International Conference on Language and Automata Theory and Applications
A bibliographical study of grammatical inference
Pattern Recognition
Learning driving behavior by timed syntactic pattern recognition
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Learning probabilistic real-time automata from multi-attribute event logs
Intelligent Data Analysis - Dynamic Networks and Knowledge Discovery
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We adapt an algorithm (RTI) for identifying (learning) a deterministic real-time automaton (DRTA) to the setting of positive timed strings (or time-stamped event sequences). An DRTA can be seen as a deterministic finite state automaton (DFA) with time constraints. Because DRTAs model time using numbers, they can be exponentially more compact than equivalent DFA models that model time using states. We use a new likelihood-ratio statistical test for checking consistency in the RTI algorithm. The result is the RTI+ algorithm, which stands for real-time identification from positive data. RTI+ is an efficient algorithm for identifying DRTAs from positive data. We show using artificial data that RTI+ is capable of identifying sufficiently large DRTAs in order to identify real-world real-time systems.