Learning two-tape automata from queries and counterexamples

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

We investigate the learning problem of two-tape deterministic finite automata (2-tape DFAs) from queries and counterexamples. Instead of accepting a subset of Σ*, a 2-tape DFA over an alphabet Σ accepts a subset of Σ*×Σ*, and therefore, it can specify a binary relation on Σ*. In Angluin showed that the class of deterministic finite automata (DFAs) is learnable in polynomial time from membership queries and equivalence queries, namely, from minimally adequate teacher (MAT). In this article we show that the class of 2-tape DFAs is learnable in polynomial time from MAT in the following sense that there effectively exists an algorithm that, given any language L accepted by an unknown 2-tape DFA M, learns from MAT a two-tape nondeterministic finite automaton (2-tape NFA) M′ accepting L in time polynomial in n and l, where n is the size of M for L and l is the maximum length of any counterexample provided during the learning process. This gives a generalization of the corresponding Angluin's result for DFAs.

Original languageEnglish
Title of host publicationProc 6 Annu ACM Conf Comput Learn Theory
Editors Anon
PublisherPubl by ACM
Pages228-235
Number of pages8
ISBN (Print)0897916115
Publication statusPublished - 1993 Dec 1
EventProceedings of the 6th Annual ACM Conference on Computational Learning Theory - Santa Cruz, CA, USA
Duration: 1993 Jul 261993 Jul 28

Publication series

NameProc 6 Annu ACM Conf Comput Learn Theory

Other

OtherProceedings of the 6th Annual ACM Conference on Computational Learning Theory
CitySanta Cruz, CA, USA
Period93/7/2693/7/28

ASJC Scopus subject areas

  • Engineering(all)

Fingerprint Dive into the research topics of 'Learning two-tape automata from queries and counterexamples'. Together they form a unique fingerprint.

  • Cite this

    Yokomori, T. (1993). Learning two-tape automata from queries and counterexamples. In Anon (Ed.), Proc 6 Annu ACM Conf Comput Learn Theory (pp. 228-235). (Proc 6 Annu ACM Conf Comput Learn Theory). Publ by ACM.