Abstract
In this work, we give an algorithm that infers Regular Trace Languages. Trace languages can be seen as regular languages that are closed under a partial commutation relation called the independence relation. This algorithm is similar to the RPNI algorithm, but it is based on Asynchronous Cellular Automata. For this purpose, we define Asynchronous Cellular Moore Machines and implement the merge operation as the calculation of an equivalence relation. After presenting the algorithm we provide a proof of its convergence (which is more complicated than the proof of convergence of the RPNI because there are no Minimal Automata for Asynchronous Automata), and we discuss the complexity of the algorithm.
Work supported by the project Técnicas de Inferencia Gramatical y aplicación al procesamiento de biosecuencias (TIN2007-60769) supported by the Spanish Ministry of Education and Sciences.
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Cano Gómez, A. (2010). Inferring Regular Trace Languages from Positive and Negative Samples. In: Sempere, J.M., García, P. (eds) Grammatical Inference: Theoretical Results and Applications. ICGI 2010. Lecture Notes in Computer Science(), vol 6339. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15488-1_3
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DOI: https://doi.org/10.1007/978-3-642-15488-1_3
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