Open this publication in new window or tab >>2005 (English)In: Electronical Notes in Theoretical Computer Science, ISSN 1571-0661, E-ISSN 1571-0661, Vol. 118, p. 3-18Article in journal (Refereed) Published
Abstract [en]
Among other domains, learning finite-state machines is important for obtaining a model of a system under development, so that powerful formal methods such as model checking can be applied.
A prominent algorithm for learning such devices was developed by Angluin. We have implemented this algorithm in a straightforward way to gain further insights to practical applicability. Furthermore, we have analyzed its performance on randomly generated as well as real-world examples. Our experiments focus on the impact of the alphabet size and the number of states on the needed number of membership queries. Additionally, we have implemented and analyzed an optimized version for learning prefix-closed regular languages. Memory consumption is one major obstacle when we attempted to learn large examples.
We see that prefix-closed languages are relatively hard to learn compared to arbitrary regular languages. The optimization, however, shows positive results.
Keywords
deterministic finite-state automata, learning algorithm, regular languages, prefix-closed regular languages
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:uu:diva-98083 (URN)10.1016/j.entcs.2004.12.015 (DOI)
2009-02-062009-02-062018-01-13Bibliographically approved