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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.

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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

DOI: 10.4230/LIPIcs.CONCUR.2021.9

Fragility and Robustness in Mean-Payoff Adversarial Stackelberg Games

Authors: Mrudula Balachander, Shibashis Guha, and Jean-François Raskin

Published in: LIPIcs, Volume 203, 32nd International Conference on Concurrency Theory (CONCUR 2021)

Abstract

Two-player mean-payoff Stackelberg games are nonzero-sum infinite duration games played on a bi-weighted graph by Leader (Player 0) and Follower (Player 1). Such games are played sequentially: first, Leader announces her strategy, second, Follower chooses his best-response. If we cannot impose which best-response is chosen by Follower, we say that Follower, though strategic, is adversarial towards Leader. The maximal value that Leader can get in this nonzero-sum game is called the adversarial Stackelberg value (ASV) of the game. We study the robustness of strategies for Leader in these games against two types of deviations: (i) Modeling imprecision - the weights on the edges of the game arena may not be exactly correct, they may be delta-away from the right one. (ii) Sub-optimal response - Follower may play epsilon-optimal best-responses instead of perfect best-responses. First, we show that if the game is zero-sum then robustness is guaranteed while in the nonzero-sum case, optimal strategies for ASV are fragile. Second, we provide a solution concept to obtain strategies for Leader that are robust to both modeling imprecision, and as well as to the epsilon-optimal responses of Follower, and study several properties and algorithmic problems related to this solution concept.

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Mrudula Balachander, Shibashis Guha, and Jean-François Raskin. Fragility and Robustness in Mean-Payoff Adversarial Stackelberg Games. In 32nd International Conference on Concurrency Theory (CONCUR 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 203, pp. 9:1-9:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{balachander_et_al:LIPIcs.CONCUR.2021.9,
  author =	{Balachander, Mrudula and Guha, Shibashis and Raskin, Jean-Fran\c{c}ois},
  title =	{{Fragility and Robustness in Mean-Payoff Adversarial Stackelberg Games}},
  booktitle =	{32nd International Conference on Concurrency Theory (CONCUR 2021)},
  pages =	{9:1--9:17},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-203-7},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{203},
  editor =	{Haddad, Serge and Varacca, Daniele},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CONCUR.2021.9},
  URN =		{urn:nbn:de:0030-drops-143863},
  doi =		{10.4230/LIPIcs.CONCUR.2021.9},
  annote =	{Keywords: mean-payoff, Stackelberg games, synthesis}
}

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Documents authored by Balachander, Mrudula

Passive Learning of Regular Data Languages in Polynomial Time and Data

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Fragility and Robustness in Mean-Payoff Adversarial Stackelberg Games

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