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  • 标题:Strongly Unambiguous Büchi Automata Are Polynomially Predictable With Membership Queries
  • 本地全文:下载
  • 作者:Dana Angluin ; Timos Antonopoulos ; Dana Fisman
  • 期刊名称:LIPIcs : Leibniz International Proceedings in Informatics
  • 电子版ISSN:1868-8969
  • 出版年度:2020
  • 卷号:152
  • 页码:1-17
  • DOI:10.4230/LIPIcs.CSL.2020.8
  • 出版社:Schloss Dagstuhl -- Leibniz-Zentrum fuer Informatik
  • 摘要:A Büchi automaton is strongly unambiguous if every word w â^^ Σ^ω has at most one final path. Many properties of strongly unambiguous Büchi automata (SUBAs) are known. They are fully expressive: every regular ω-language can be represented by a SUBA. Equivalence and containment of SUBAs can be decided in polynomial time. SUBAs may be exponentially smaller than deterministic Muller automata and may be exponentially bigger than deterministic Büchi automata. In this work we show that SUBAs can be learned in polynomial time using membership and certain non-proper equivalence queries, which implies that they are polynomially predictable with membership queries. In contrast, under plausible cryptographic assumptions, non-deterministic Büchi automata are not polynomially predictable with membership queries.
  • 关键词:Polynomially predictable languages; Automata learning; Strongly unambiguous Büchi automata; Automata succinctness; Regular ω-languages; Grammatical
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