6533b85ffe1ef96bd12c2718
RESEARCH PRODUCT
Gl-learning
Marco OrtolaniGabriele PergolaPietro Cottonesubject
Theoretical computer scienceComputer sciencemedia_common.quotation_subjectParallel algorithm0102 computer and information sciences02 engineering and technologycomputer.software_genre01 natural sciencesField (computer science)Grammatical inferenceSoftwareKnowledge extractionSoftware library0202 electrical engineering electronic engineering information engineering1707media_commonSettore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniGrammarbusiness.industryProgramming languageModular designGrammar inductionHuman-Computer InteractionParallel algorithmRange (mathematics)Computer Networks and Communication010201 computation theory & mathematics020201 artificial intelligence & image processingbusinesscomputerSoftwaredescription
In this paper, we present a new open-source software library, Gl-learning, for grammatical inference. The rise of new application scenarios in recent years has required optimized methods to address knowledge extraction from huge amounts of data and to model highly complex systems. Our library implements the main state-of-the-art algorithms in the grammatical inference field (RPNI, EDSM, L*), redesigned through the OpenMP library for a parallel execution that drastically decreases execution times. To our best knowledge, it is also the first comprehensive library including a noise tolerance learning algorithm, such as Blue*, that significantly broadens the range of the potential application scenarios for grammar models. The modular design of our C++ library makes it an efficient and extensible framework for the design of further novel algorithms.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2016-06-23 | Proceedings of the 17th International Conference on Computer Systems and Technologies 2016 |