Learning and Model-Checking Networks of I/O Automata
Proceedings of the Asian Conference on Machine Learning, PMLR 25:285-300, 2012.
We introduce a new statistical relational learning (SRL) approach in which models for structured data, especially network data, are constructed as networks of communicating finite 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 afforded by learning generic templates enables one to apply the learned model to new domains. A main benefit of learning models based on finite 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.