Learning Product Automata

Research output: Chapter in Book/Report/Conference proceedingConference Article in proceedingAcademicpeer-review

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.
Original languageEnglish
Title of host publicationProceedings of Machine Learning Research
EditorsOlgierd Unold, Witold Dyrka, Wojciech Wieczorek
PublisherPMLR
Pages54-66
Number of pages13
Volume93
Publication statusPublished - 2019
Externally publishedYes
EventThe 14th International Conference on Grammatical Inference - Wrocław, Poland
Duration: 5 Sep 20187 Sep 2018
Conference number: 14
http://icgi2018.pwr.edu.pl/papers

Conference

ConferenceThe 14th International Conference on Grammatical Inference
Abbreviated titleICGI2018
Country/TerritoryPoland
CityWrocław
Period5/09/187/09/18
Internet address

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