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 = {Ali Khalili and Armando Tacchella}, booktitle = {The 12th International Conference on Grammatical Inference}, pages = {109--123}, year = {2014}, editor = {Alexander Clark and Makoto Kanazawa and Ryo Yoshinaka}, 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 = {http://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 %J Proceedings of Machine Learning Research %P 109--123 %U http://proceedings.mlr.press %V 34 %W PMLR %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 PY - 2014/08/30 DA - 2014/08/30 ED - Alexander Clark ED - Makoto Kanazawa ED - Ryo Yoshinaka ID - pmlr-v34-khalili14a PB - PMLR SP - 109 DP - PMLR EP - 123 L1 - http://proceedings.mlr.press/v34/khalili14a.pdf UR - http://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 PMLR 34:109-123

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