State Matching and Multiple References in Adaptive Active Automata Learning
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866929402693025792 |
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| author | Kruger, Loes Junges, Sebastian Rot, Jurriaan |
| author_facet | Kruger, Loes Junges, Sebastian Rot, Jurriaan |
| contents | Active automata learning (AAL) is a method to infer state machines by interacting with black-box systems. Adaptive AAL aims to reduce the sample complexity of AAL by incorporating domain specific knowledge in the form of (similar) reference models. Such reference models appear naturally when learning multiple versions or variants of a software system. In this paper, we present state matching, which allows flexible use of the structure of these reference models by the learner. State matching is the main ingredient of adaptive L#, a novel framework for adaptive learning, built on top of L#. Our empirical evaluation shows that adaptive L# improves the state of the art by up to two orders of magnitude. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_19714 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | State Matching and Multiple References in Adaptive Active Automata Learning Kruger, Loes Junges, Sebastian Rot, Jurriaan Logic in Computer Science Machine Learning Active automata learning (AAL) is a method to infer state machines by interacting with black-box systems. Adaptive AAL aims to reduce the sample complexity of AAL by incorporating domain specific knowledge in the form of (similar) reference models. Such reference models appear naturally when learning multiple versions or variants of a software system. In this paper, we present state matching, which allows flexible use of the structure of these reference models by the learner. State matching is the main ingredient of adaptive L#, a novel framework for adaptive learning, built on top of L#. Our empirical evaluation shows that adaptive L# improves the state of the art by up to two orders of magnitude. |
| title | State Matching and Multiple References in Adaptive Active Automata Learning |
| topic | Logic in Computer Science Machine Learning |
| url | https://arxiv.org/abs/2406.19714 |