3 Search Results for "Bohn, León"

Document
DOI: 10.4230/LIPIcs.CONCUR.2024.10
Passive Learning of Regular Data Languages in Polynomial Time and Data

Authors: Mrudula Balachander, Emmanuel Filiot, and Raffaella Gentilini

Published in: LIPIcs, Volume 311, 35th International Conference on Concurrency Theory (CONCUR 2024)

Abstract
A regular data language is a language over an infinite alphabet recognized by a deterministic register automaton (DRA), as defined by Benedikt, Ley and Puppis. The later model, which is expressively equivalent to the deterministic finite-memory automata introduced earlier by Francez and Kaminsky, enjoys unique minimal automata (up to isomorphism), based on a Myhill-Nerode theorem. In this paper, we introduce a polynomial time passive learning algorithm for regular data languages from positive and negative samples. Following Gold’s model for learning languages, we prove that our algorithm can identify in the limit any regular data language L, i.e. it returns a minimal DRA recognizing L if a characteristic sample set for L is provided as input. We prove that there exist characteristic sample sets of polynomial size with respect to the size of the minimal DRA recognizing L. To the best of our knowledge, it is the first passive learning algorithm for data languages, and the first learning algorithm which is fully polynomial, both with respect to time complexity and size of the characteristic sample set.
Cite as

Mrudula Balachander, Emmanuel Filiot, and Raffaella Gentilini. Passive Learning of Regular Data Languages in Polynomial Time and Data. In 35th International Conference on Concurrency Theory (CONCUR 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 311, pp. 10:1-10:21, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{balachander_et_al:LIPIcs.CONCUR.2024.10,
  author =	{Balachander, Mrudula and Filiot, Emmanuel and Gentilini, Raffaella},
  title =	{{Passive Learning of Regular Data Languages in Polynomial Time and Data}},
  booktitle =	{35th International Conference on Concurrency Theory (CONCUR 2024)},
  pages =	{10:1--10:21},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-339-3},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{311},
  editor =	{Majumdar, Rupak and Silva, Alexandra},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CONCUR.2024.10},
  URN =		{urn:nbn:de:0030-drops-207829},
  doi =		{10.4230/LIPIcs.CONCUR.2024.10},
  annote =	{Keywords: Register automata, passive learning, automata over infinite alphabets}
}
Document
Track B: Automata, Logic, Semantics, and Theory of Programming
DOI: 10.4230/LIPIcs.ICALP.2022.114
Passive Learning of Deterministic Büchi Automata by Combinations of DFAs

Authors: León Bohn and Christof Löding

Published in: LIPIcs, Volume 229, 49th International Colloquium on Automata, Languages, and Programming (ICALP 2022)

Abstract
We present an algorithm that constructs a deterministic Büchi automaton in polynomial time from given sets of positive and negative example words. This learner constructs multiple DFAs using a polynomial-time active learning algorithm on finite words as black box using an oracle that we implement based on the given sample of ω-words, and combines these DFAs into a single DBA. We prove that the resulting algorithm can learn a DBA for each DBA-recognizable language in the limit by providing a characteristic sample for each DBA-recognizable language. We can only guarantee completeness of our algorithm for the full class of DBAs through characteristic samples that are, in general, exponential in the size of a minimal DBA for the target language. But we show that for each fixed k these characteristic samples are of polynomial size for the class of DBAs in which each subset of pairwise language-equivalent states has size at most k.
Cite as

León Bohn and Christof Löding. Passive Learning of Deterministic Büchi Automata by Combinations of DFAs. In 49th International Colloquium on Automata, Languages, and Programming (ICALP 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 229, pp. 114:1-114:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{bohn_et_al:LIPIcs.ICALP.2022.114,
  author =	{Bohn, Le\'{o}n and L\"{o}ding, Christof},
  title =	{{Passive Learning of Deterministic B\"{u}chi Automata by Combinations of DFAs}},
  booktitle =	{49th International Colloquium on Automata, Languages, and Programming (ICALP 2022)},
  pages =	{114:1--114:20},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-235-8},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{229},
  editor =	{Boja\'{n}czyk, Miko{\l}aj and Merelli, Emanuela and Woodruff, David P.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ICALP.2022.114},
  URN =		{urn:nbn:de:0030-drops-164553},
  doi =		{10.4230/LIPIcs.ICALP.2022.114},
  annote =	{Keywords: deterministic B\"{u}chi automata, learning from examples, learning in the limit, active learning}
}
Document
DOI: 10.4230/LIPIcs.MFCS.2021.20
Constructing Deterministic ω-Automata from Examples by an Extension of the RPNI Algorithm

Authors: León Bohn and Christof Löding

Published in: LIPIcs, Volume 202, 46th International Symposium on Mathematical Foundations of Computer Science (MFCS 2021)

Abstract
The RPNI algorithm (Oncina, Garcia 1992) constructs deterministic finite automata from finite sets of negative and positive example words. We propose and analyze an extension of this algorithm to deterministic ω-automata with different types of acceptance conditions. In order to obtain this generalization of RPNI, we develop algorithms for the standard acceptance conditions of ω-automata that check for a given set of example words and a deterministic transition system, whether these example words can be accepted in the transition system with a corresponding acceptance condition. Based on these algorithms, we can define the extension of RPNI to infinite words. We prove that it can learn all deterministic ω-automata with an informative right congruence in the limit with polynomial time and data. We also show that the algorithm, while it can learn some automata that do not have an informative right congruence, cannot learn deterministic ω-automata for all regular ω-languages in the limit. Finally, we also prove that active learning with membership and equivalence queries is not easier for automata with an informative right congruence than for general deterministic ω-automata.
Cite as

León Bohn and Christof Löding. Constructing Deterministic ω-Automata from Examples by an Extension of the RPNI Algorithm. In 46th International Symposium on Mathematical Foundations of Computer Science (MFCS 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 202, pp. 20:1-20:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{bohn_et_al:LIPIcs.MFCS.2021.20,
  author =	{Bohn, Le\'{o}n and L\"{o}ding, Christof},
  title =	{{Constructing Deterministic \omega-Automata from Examples by an Extension of the RPNI Algorithm}},
  booktitle =	{46th International Symposium on Mathematical Foundations of Computer Science (MFCS 2021)},
  pages =	{20:1--20:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-201-3},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{202},
  editor =	{Bonchi, Filippo and Puglisi, Simon J.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.MFCS.2021.20},
  URN =		{urn:nbn:de:0030-drops-144607},
  doi =		{10.4230/LIPIcs.MFCS.2021.20},
  annote =	{Keywords: deterministic omega-automata, learning from examples, learning in the limit, constructing acceptance conditions, active learning}
}
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