Learning Mealy machines with one timer

Frits Vaandrager; Masoud Ebrahimi; Roderick Bloem

doi:10.1016/j.ic.2023.105013

Learning Mealy machines with one timer

Frits Vaandrager, Masoud Ebrahimi^*, Roderick Bloem

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

Abstract

We present Mealy machines with a single timer (MM1Ts), a class of sufficiently expressive models to describe the real-time behavior of many realistic applications that we can learn efficiently. We show how we can obtain learning algorithms for MM1Ts via a reduction to the problem of learning Mealy machines. We describe an implementation of an MM1T learner on top of LearnLib and compare its performance with recent algorithms proposed by Aichernig et al. and An et al. on several realistic benchmarks.

Original language	English
Article number	105013
Number of pages	21
Journal	Information and Computation
Volume	295
Early online date	20 Feb 2023
DOIs	https://doi.org/10.1016/j.ic.2023.105013
Publication status	Published - Dec 2023

ASJC Scopus subject areas

Theoretical Computer Science
Information Systems
Computer Science Applications
Computational Theory and Mathematics

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https://www.sciencedirect.com/science/article/pii/S0890540123000147Licence: CC BY-NC-ND 4.0

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title = "Learning Mealy machines with one timer",

abstract = "We present Mealy machines with a single timer (MM1Ts), a class of sufficiently expressive models to describe the real-time behavior of many realistic applications that we can learn efficiently. We show how we can obtain learning algorithms for MM1Ts via a reduction to the problem of learning Mealy machines. We describe an implementation of an MM1T learner on top of LearnLib and compare its performance with recent algorithms proposed by Aichernig et al. and An et al. on several realistic benchmarks.",

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AB - We present Mealy machines with a single timer (MM1Ts), a class of sufficiently expressive models to describe the real-time behavior of many realistic applications that we can learn efficiently. We show how we can obtain learning algorithms for MM1Ts via a reduction to the problem of learning Mealy machines. We describe an implementation of an MM1T learner on top of LearnLib and compare its performance with recent algorithms proposed by Aichernig et al. and An et al. on several realistic benchmarks.

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Learning Mealy machines with one timer

Abstract

ASJC Scopus subject areas

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