Learning Nondeterministic Mealy Machines

Ali Khalili, Armando Tacchella
The 12th International Conference on Grammatical Inference, PMLR 34:109-123, 2014.

Abstract

In applications where abstract models of reactive systems are to be inferred, one important challenge is that the behavior of such systems can be inherently nondeterministic. To cope with this challenge, we developed an algorithm to infer nondeterministic computation models in the form of Mealy machines. We introduce our approach and provide extensive experimental results to assess its potential in the identification of black-box reactive systems. The experiments involve both artificially-generated abstract Mealy machines, and the identification of a TFTP server model starting from a publicly-available implementation.

Cite this Paper

BibTeX
@InProceedings{pmlr-v34-khalili14a, title = {Learning Nondeterministic Mealy Machines}, author = {Khalili, Ali and Tacchella, Armando}, booktitle = {The 12th International Conference on Grammatical Inference}, pages = {109--123}, year = {2014}, editor = {Clark, Alexander and Kanazawa, Makoto and Yoshinaka, Ryo}, volume = {34}, series = {Proceedings of Machine Learning Research}, address = {Kyoto, Japan}, month = {17--19 Sep}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v34/khalili14a.pdf}, url = {https://proceedings.mlr.press/v34/khalili14a.html}, abstract = {In applications where abstract models of reactive systems are to be inferred, one important challenge is that the behavior of such systems can be inherently nondeterministic. To cope with this challenge, we developed an algorithm to infer nondeterministic computation models in the form of Mealy machines. We introduce our approach and provide extensive experimental results to assess its potential in the identification of black-box reactive systems. The experiments involve both artificially-generated abstract Mealy machines, and the identification of a TFTP server model starting from a publicly-available implementation. } }
Endnote
%0 Conference Paper %T Learning Nondeterministic Mealy Machines %A Ali Khalili %A Armando Tacchella %B The 12th International Conference on Grammatical Inference %C Proceedings of Machine Learning Research %D 2014 %E Alexander Clark %E Makoto Kanazawa %E Ryo Yoshinaka %F pmlr-v34-khalili14a %I PMLR %P 109--123 %U https://proceedings.mlr.press/v34/khalili14a.html %V 34 %X In applications where abstract models of reactive systems are to be inferred, one important challenge is that the behavior of such systems can be inherently nondeterministic. To cope with this challenge, we developed an algorithm to infer nondeterministic computation models in the form of Mealy machines. We introduce our approach and provide extensive experimental results to assess its potential in the identification of black-box reactive systems. The experiments involve both artificially-generated abstract Mealy machines, and the identification of a TFTP server model starting from a publicly-available implementation.
RIS
TY - CPAPER TI - Learning Nondeterministic Mealy Machines AU - Ali Khalili AU - Armando Tacchella BT - The 12th International Conference on Grammatical Inference DA - 2014/08/30 ED - Alexander Clark ED - Makoto Kanazawa ED - Ryo Yoshinaka ID - pmlr-v34-khalili14a PB - PMLR DP - Proceedings of Machine Learning Research VL - 34 SP - 109 EP - 123 L1 - http://proceedings.mlr.press/v34/khalili14a.pdf UR - https://proceedings.mlr.press/v34/khalili14a.html AB - In applications where abstract models of reactive systems are to be inferred, one important challenge is that the behavior of such systems can be inherently nondeterministic. To cope with this challenge, we developed an algorithm to infer nondeterministic computation models in the form of Mealy machines. We introduce our approach and provide extensive experimental results to assess its potential in the identification of black-box reactive systems. The experiments involve both artificially-generated abstract Mealy machines, and the identification of a TFTP server model starting from a publicly-available implementation. ER -
APA
Khalili, A. & Tacchella, A.. (2014). Learning Nondeterministic Mealy Machines. The 12th International Conference on Grammatical Inference, in Proceedings of Machine Learning Research 34:109-123 Available from https://proceedings.mlr.press/v34/khalili14a.html.

Related Material