Learning and Model-Checking Networks of I/O Automata

Hua Mao, Manfred Jaeger
Proceedings of the Asian Conference on Machine Learning, PMLR 25:285-300, 2012.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v25-mao12, title = {Learning and Model-Checking Networks of {I/O} Automata}, author = {Mao, Hua and Jaeger, Manfred}, booktitle = {Proceedings of the Asian Conference on Machine Learning}, pages = {285--300}, year = {2012}, editor = {Hoi, Steven C. H. and Buntine, Wray}, volume = {25}, series = {Proceedings of Machine Learning Research}, address = {Singapore Management University, Singapore}, month = {04--06 Nov}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v25/mao12/mao12.pdf}, url = {https://proceedings.mlr.press/v25/mao12.html}, 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.} }
Endnote
%0 Conference Paper %T Learning and Model-Checking Networks of I/O Automata %A Hua Mao %A Manfred Jaeger %B Proceedings of the Asian Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2012 %E Steven C. H. Hoi %E Wray Buntine %F pmlr-v25-mao12 %I PMLR %P 285--300 %U https://proceedings.mlr.press/v25/mao12.html %V 25 %X 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.
RIS
TY - CPAPER TI - Learning and Model-Checking Networks of I/O Automata AU - Hua Mao AU - Manfred Jaeger BT - Proceedings of the Asian Conference on Machine Learning DA - 2012/11/17 ED - Steven C. H. Hoi ED - Wray Buntine ID - pmlr-v25-mao12 PB - PMLR DP - Proceedings of Machine Learning Research VL - 25 SP - 285 EP - 300 L1 - http://proceedings.mlr.press/v25/mao12/mao12.pdf UR - https://proceedings.mlr.press/v25/mao12.html AB - 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. ER -
APA
Mao, H. & Jaeger, M.. (2012). Learning and Model-Checking Networks of I/O Automata. Proceedings of the Asian Conference on Machine Learning, in Proceedings of Machine Learning Research 25:285-300 Available from https://proceedings.mlr.press/v25/mao12.html.

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