Evaluating Learning Algorithms for Stochastic Finite Automata - Comparative Empirical Analyses on Learning Models for Technical Systems

Asmir Vodenčarević, Alexander Maier, Oliver Niggemann

Abstract

Finite automata are used to model a large variety of technical systems and form the basis of important tasks such as model-based development, early simulations and model-based diagnosis. However, such models are today still mostly derived manually, in an expensive and time-consuming manner. Therefore in the past twenty years, several successful algorithms have been developed for learning various types of finite automata. These algorithms use measurements of the technical systems to automatically derive the underlying automata models. However, today users face a serious problem when looking for such model learning algorithm: Which algorithm to choose for which problem and which technical system? This papers closes this gap by comparative empirical analyses of the most popular algorithms (i) using two real-world production facilities and (ii) using artificial datasets to analyze the algorithms’ convergence and scalability. Finally, based on these results, several observations for choosing an appropriate automaton learning algorithm for a specific problem are given.

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Paper Citation


in Harvard Style

Vodenčarević A., Maier A. and Niggemann O. (2013). Evaluating Learning Algorithms for Stochastic Finite Automata - Comparative Empirical Analyses on Learning Models for Technical Systems . In Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-8565-41-9, pages 229-238. DOI: 10.5220/0004255702290238


in Bibtex Style

@conference{icpram13,
author={Asmir Vodenčarević and Alexander Maier and Oliver Niggemann},
title={Evaluating Learning Algorithms for Stochastic Finite Automata - Comparative Empirical Analyses on Learning Models for Technical Systems},
booktitle={Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2013},
pages={229-238},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004255702290238},
isbn={978-989-8565-41-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Evaluating Learning Algorithms for Stochastic Finite Automata - Comparative Empirical Analyses on Learning Models for Technical Systems
SN - 978-989-8565-41-9
AU - Vodenčarević A.
AU - Maier A.
AU - Niggemann O.
PY - 2013
SP - 229
EP - 238
DO - 10.5220/0004255702290238