L*-Based Learning of Markov Decision Processes

Martin Tappler*, Bernhard K. Aichernig, Giovanni Bacci, Maria Eichlseder, Kim Guldstrand Larsen

*Kontaktforfatter

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

5 Citationer (Scopus)
11 Downloads (Pure)

Abstrakt

Automata learning techniques automatically generate system models from test observations. These techniques usually fall into two categories: passive and active. Passive learning uses a predetermined data set, e.g., system logs. In contrast, active learning actively queries the system under learning, which is considered more efficient.
An influential active learning technique is Angluin’s L* algorithm for regular languages which inspired several generalisations from DFAs to other automata-based modelling formalisms. In this work, we study L*-based learning of deterministic Markov decision processes, first assuming an ideal setting with perfect information. Then, we relax this assumption and present a novel learning algorithm that collects information by sampling system traces via testing. Experiments with the implementation of our sampling-based algorithm suggest that it achieves better accuracy than state-of-the-art passive learning techniques with the same amount of test data. Unlike existing learning algorithms with predefined states, our algorithm learns the complete model structure including the states.
OriginalsprogEngelsk
TitelFormal Methods – The Next 30 Years - 3rd World Congress, FM 2019, Proceedings : FM 2019: Formal Methods – The Next 30 Years
RedaktørerMaurice H. ter Beek, Annabelle McIver, José N. Oliveira
Antal sider19
ForlagSpringer
Publikationsdato23 sep. 2019
Sider651-669
ISBN (Trykt)978-3-030-30941-1
ISBN (Elektronisk)978-3-030-30942-8
DOI
StatusUdgivet - 23 sep. 2019
BegivenhedInternational Symposium on Formal Methods: Formal Methods – The Next 30 Years - Porto, Portugal
Varighed: 7 okt. 201911 okt. 2019
http://formalmethods2019.inesctec.pt

Konference

KonferenceInternational Symposium on Formal Methods
LandPortugal
ByPorto
Periode07/10/201911/10/2019
Internetadresse
NavnLecture Notes in Computer Science
Vol/bind11800
ISSN0302-9743

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