Learning and Model-checking Networks of I/O Automata

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

2 Citations (Scopus)


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.
Original languageEnglish
Title of host publicationProceeding of the 4th Asian Conference on Machine Learning (ACML 2012)
EditorsSteven C. H. Hoi, Wray Buntine
Publication date2012
Publication statusPublished - 2012
EventAsian Conference on Machine Learning - Singapore, Singapore
Duration: 4 Nov 20126 Nov 2012
Conference number: 4


ConferenceAsian Conference on Machine Learning
SeriesJMLR Workshop and Conference Proceedings


Cite this

Mao, H., & Jaeger, M. (2012). Learning and Model-checking Networks of I/O Automata. In S. C. H. Hoi, & W. Buntine (Eds.), Proceeding of the 4th Asian Conference on Machine Learning (ACML 2012) (pp. 285-300). JMLR Workshop and Conference Proceedings, Vol.. 25