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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2405.08647 |
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| _version_ | 1866917665546698752 |
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| author | Koenders, Rick Moerman, Joshua |
| author_facet | Koenders, Rick Moerman, Joshua |
| contents | We present an active automata learning algorithm which learns a decomposition of a finite state machine, based on projecting onto individual outputs. This is dual to a recent compositional learning algorithm by Labbaf et al. (2023). When projecting the outputs to a smaller set, the model itself is reduced in size. By having several such projections, we do not lose any information and the full system can be reconstructed. Depending on the structure of the system this reduces the number of queries drastically, as shown by a preliminary evaluation of the algorithm. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_08647 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Output-decomposed Learning of Mealy Machines Koenders, Rick Moerman, Joshua Logic in Computer Science Machine Learning We present an active automata learning algorithm which learns a decomposition of a finite state machine, based on projecting onto individual outputs. This is dual to a recent compositional learning algorithm by Labbaf et al. (2023). When projecting the outputs to a smaller set, the model itself is reduced in size. By having several such projections, we do not lose any information and the full system can be reconstructed. Depending on the structure of the system this reduces the number of queries drastically, as shown by a preliminary evaluation of the algorithm. |
| title | Output-decomposed Learning of Mealy Machines |
| topic | Logic in Computer Science Machine Learning |
| url | https://arxiv.org/abs/2405.08647 |