Sicco Verwer
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My interests lie in the fields of learning theory, grammatical inference, complexity theory, and information theory. My main goal is to write efficient algorithms for the identification of (learning) finite state machines. I want to identify the entire structure of such a machine, not just its parameters given the structure.

I try to identify machines known as timed automata. These automata include timing relations explicitly, i.e. using numbers. The reasons for identifying these models are twofold: Firstly, they can model systems in an intuitively appealing way. Secondly, we believe that identifyig such models from timed data is easier (i.e. more efficient) then identifying a model that models time implicitly (such as deterministic finite state automata and hidden Markov models).

My research focusses on the complexity of identifying and teaching the class of timed automata. Due to negative results regarding these complexities, we have written an algorithm for identifying a simple type of timed automaton, known as a real-time automaton. We have shown experimentally that this algorithm outperforms a similar method that idenitifies a deterministic finite state automaton from the same data when sampling at some fixed frequency.


flag Polynomial distinguishability of timed automata
as author at  9th International Colloquium on Grammatical Inference (ICGI), Saint-Malo 2008,