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Correctness and Performance of an Incremental Learning Algorithm for Finite Automata
KTH, School of Computer Science and Communication (CSC), Theoretical Computer Science, TCS.ORCID iD: 0000-0002-9706-5008
KTH, School of Computer Science and Communication (CSC), Theoretical Computer Science, TCS.
2011 (English)Conference paper, Poster (with or without abstract) (Other academic)
Abstract [en]

We present a new algorithm IDS for incremental learning of deterministic finite automata (DFA). This algorithm is based on the concept of distinguishing sequences introduced in (Angluin, 1981). We give a rigorous proof that two versions of this learning algorithm correctly learn in the limit. Finally we present an empirical performance analysis that compares these two algorithms, focussing on learning times and different types of learning queries. We conclude that IDS is an efficient algorithm for software engineering applications of automata learning, such as testing and model inference.

Place, publisher, year, edition, pages
2011.
Keyword [en]
incremental learning, learning in the limit, distinguishing sequences, DFA learning
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-119089OAI: oai:DiVA.org:kth-119089DiVA: diva2:609697
Conference
Third Asian Conference on Machine Learning
Note

QC 20130312

Available from: 2013-03-06 Created: 2013-03-06 Last updated: 2013-03-12Bibliographically approved

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Meinke, Karl

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