Learning Product Automata

Joshua Moerman
Proceedings of The 14th International Conference on Grammatical Inference 2018, PMLR 93:54-66, 2019.

Abstract

We give an optimisation for active learning algorithms, applicable to learning Moore machines with decomposable outputs. These machines can be decomposed themselves by projecting on each output. This results in smaller components that can then be learnt with fewer queries. We give experimental evidence that this is a useful technique which can reduce the number of queries substantially. Only in some cases the performance is worsened by the slight overhead. Compositional methods are widely used throughout engineering, and the decomposition presented in this article promises to be particularly interesting for learning hardware systems.

Cite this Paper

BibTeX
@InProceedings{pmlr-v93-moerman19a, title = {Learning Product Automata}, author = {Moerman, Joshua}, booktitle = {Proceedings of The 14th International Conference on Grammatical Inference 2018}, pages = {54--66}, year = {2019}, editor = {Unold, Olgierd and Dyrka, Witold and Wieczorek, Wojciech}, volume = {93}, series = {Proceedings of Machine Learning Research}, month = {feb}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v93/moerman19a/moerman19a.pdf}, url = {https://proceedings.mlr.press/v93/moerman19a.html}, abstract = {We give an optimisation for active learning algorithms, applicable to learning Moore machines with decomposable outputs. These machines can be decomposed themselves by projecting on each output. This results in smaller components that can then be learnt with fewer queries. We give experimental evidence that this is a useful technique which can reduce the number of queries substantially. Only in some cases the performance is worsened by the slight overhead. Compositional methods are widely used throughout engineering, and the decomposition presented in this article promises to be particularly interesting for learning hardware systems.} }
Endnote
%0 Conference Paper %T Learning Product Automata %A Joshua Moerman %B Proceedings of The 14th International Conference on Grammatical Inference 2018 %C Proceedings of Machine Learning Research %D 2019 %E Olgierd Unold %E Witold Dyrka %E Wojciech Wieczorek %F pmlr-v93-moerman19a %I PMLR %P 54--66 %U https://proceedings.mlr.press/v93/moerman19a.html %V 93 %X We give an optimisation for active learning algorithms, applicable to learning Moore machines with decomposable outputs. These machines can be decomposed themselves by projecting on each output. This results in smaller components that can then be learnt with fewer queries. We give experimental evidence that this is a useful technique which can reduce the number of queries substantially. Only in some cases the performance is worsened by the slight overhead. Compositional methods are widely used throughout engineering, and the decomposition presented in this article promises to be particularly interesting for learning hardware systems.
APA
Moerman, J.. (2019). Learning Product Automata. Proceedings of The 14th International Conference on Grammatical Inference 2018, in Proceedings of Machine Learning Research 93:54-66 Available from https://proceedings.mlr.press/v93/moerman19a.html.

Related Material