Learning Pairwise Disjoint Simple Languages from Positive Examples

A classical problem in grammatical inference is to identify a deterministic finite automaton (DFA) from a set of positive and negative examples. In this paper, we address the related - yet seemingly novel - problem of identifying a set of DFAs from examples that belong to different unknown simple re...

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Bibliographic Details
Main Authors: Linard, Alexis, Smetsers, Rick, Vaandrager, Frits, Waqas, Umar, van Pinxten, Joost, Verwer, Sicco
Format: Report
Language:unknown
Published: arXiv 2017
Subjects:
Machine Learning cs.LG
Formal Languages and Automata Theory cs.FL
FOS Computer and information sciences
Iceland
Online Access:https://dx.doi.org/10.48550/arxiv.1706.01663
https://arxiv.org/abs/1706.01663
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Summary:A classical problem in grammatical inference is to identify a deterministic finite automaton (DFA) from a set of positive and negative examples. In this paper, we address the related - yet seemingly novel - problem of identifying a set of DFAs from examples that belong to different unknown simple regular languages. We propose two methods based on compression for clustering the observed positive examples. We apply our methods to a set of print jobs submitted to large industrial printers. : This paper has been accepted at the Learning and Automata (LearnAut) Workshop, LICS 2017 (Reykjavik, Iceland)

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