@inproceedings{8b6a14b47b444aec9410f6f5042f472d,

title = "Learning and model-checking networks of I/O automata",

abstract = "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.",

keywords = "Automata learning, Net- work data, Probabilistic model checking, Relational learning",

author = "Hua Mao and Manfred Jaeger",

year = "2012",

language = "English",

volume = "25",

series = "Proceedings of Machine Learning Research",

publisher = "MLResearchPress",

pages = "285--300",

editor = "Hoi, {Steven C. H.} and Wray Buntine",

booktitle = "Proceedings of the Asian Conference on Machine Learning",

note = "4th Asian Conference on Machine Learning, ACML 2012 ; Conference date: 04-11-2012 Through 06-11-2012",

}