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Gl-learning: an optimized framework for grammatical inference

  • Autori: Cottone, P.; Ortolani, M.; Pergola, G.
  • Anno di pubblicazione: 2016
  • Tipologia: Contributo in atti di convegno pubblicato in volume
  • OA Link:


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 Bluethat 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.