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It provides a wide range of state-of-the-art algorithms for different automaton types ranging from fully deterministic to probabilistic automata. In this work, we present the recent addition of a generalized implementation of an important method from the domain of passive automata learning: state-merging in the red-blue framework. Using a common internal representation for different automaton types allows for a general and highly configurable implementation of the red-blue framework. We describe how to define and execute state-merging algorithms using AALpy, which reduces the implementation effort for state-merging algorithms mainly to the definition of compatibility criteria and scoring. This aids the implementation of both existing and novel algorithms. In particular, defining some existing state-merging algorithms from the literature with AALpy only takes a few lines of code.<\/jats:p>","DOI":"10.1007\/978-3-031-98685-7_6","type":"book-chapter","created":{"date-parts":[[2025,7,22]],"date-time":"2025-07-22T03:31:33Z","timestamp":1753155093000},"page":"127-140","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Extending AALpy with\u00a0Passive Learning: A Generalized State-Merging Approach"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-3595-4715","authenticated-orcid":false,"given":"Benjamin","family":"von Berg","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3484-5584","authenticated-orcid":false,"given":"Bernhard K.","family":"Aichernig","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,7,23]]},"reference":[{"key":"6_CR1","doi-asserted-by":"publisher","unstructured":"Aarts, F., Heidarian, F., Kuppens, H., Olsen, P., Vaandrager, F.W.: Automata learning through counterexample guided abstraction refinement. 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