Flexible State-Merging for learning (P)DFAs in Python

Christian Hammerschmidt, Benjamin Loos, Radu State, Thomas Engel, Sicco Verwer

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review


We present a Python package for learning (non-)probabilistic deterministic nite state automata and provide heuristics in the red-blue framework. As our package is built along the API of the popular scikit-learn package, it is easy to use and new learning methods are easy to add. It provides PDFA learning as an additional tool for sequence prediction or classication to data scientists, without the need to understand the algorithm itself but rather the limitations of PDFA as a model. With applications of automata learning in diverse elds such as network trac analysis, software engineering and biology, a stratied package opens opportunities for practitioners.
Original languageEnglish
Title of host publicationProceedings of The 13th International Conference on Grammatical Inference
Subtitle of host publicationThe JMLR Workshop and Conference, The Sequence PredictIction ChallengE (SPiCe)
EditorsS. Verwer, M. van Zaanen, R. Smetsers
Number of pages6
Publication statusPublished - 2016
Event13th International Conference on Grammatical Inference, ICGI 2016 - Delft, Netherlands
Duration: 5 Oct 20167 Oct 2016

Publication series

NameJMLR: Workshop and Conference Proceedings
ISSN (Electronic)1938-7228


Conference13th International Conference on Grammatical Inference, ICGI 2016
Abbreviated titleICGI


  • machine learning
  • grammar inference
  • automaton learning
  • PDFA inference


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