Learning and Model-checking Networks of I/O Automata

We introduce a new statistical relational learning (SRL) approach in which models for
structured data, especially network data, are constructed as networks of communicating
nite probabilistic automata. Leveraging existing automata learning methods from the area
of grammatical inference, we can learn generic models for network entities in the form of
automata templates. As is characteristic for SRL techniques, the abstraction level aorded
by learning generic templates enables one to apply the learned model to new domains. A
main benet of learning models based on nite automata lies in the fact that one can analyse
the resulting models using formal model-checking techniques, which adds a dimension of
model analysis not usually available for traditional SRL modeling frameworks.