Abstract
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.
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.
Original language | English |
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Title of host publication | Formal Methods – The Next 30 Years - 3rd World Congress, FM 2019, Proceedings : FM 2019: Formal Methods – The Next 30 Years |
Editors | Maurice H. ter Beek, Annabelle McIver, José N. Oliveira |
Number of pages | 19 |
Publisher | Springer |
Publication date | 23 Sept 2019 |
Pages | 651-669 |
ISBN (Print) | 978-3-030-30941-1 |
ISBN (Electronic) | 978-3-030-30942-8 |
DOIs | |
Publication status | Published - 23 Sept 2019 |
Event | International Symposium on Formal Methods: Formal Methods – The Next 30 Years - Porto, Portugal Duration: 7 Oct 2019 → 11 Oct 2019 http://formalmethods2019.inesctec.pt |
Conference
Conference | International Symposium on Formal Methods |
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Country/Territory | Portugal |
City | Porto |
Period | 07/10/2019 → 11/10/2019 |
Internet address |
Series | Lecture Notes in Computer Science |
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Volume | 11800 |
ISSN | 0302-9743 |
Keywords
- Active automata learning
- Markov decision processes
- Model inference