{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/learning-moore-machines-from-input-output","title":"Learning Moore Machines from Input-Output Traces","arxiv_id":"1605.07805","date":"2016-05-25","proceeding":null,"authors":["Georgios Giantamidis","Stavros Tripakis"],"abstract":"The problem of learning automata from example traces (but no equivalence or\nmembership queries) is fundamental in automata learning theory and practice. In\nthis paper we study this problem for finite state machines with inputs and\noutputs, and in particular for Moore machines. We develop three algorithms for\nsolving this problem: (1) the PTAP algorithm, which transforms a set of\ninput-output traces into an incomplete Moore machine and then completes the\nmachine with self-loops; (2) the PRPNI algorithm, which uses the well-known\nRPNI algorithm for automata learning to learn a product of automata encoding a\nMoore machine; and (3) the MooreMI algorithm, which directly learns a Moore\nmachine using PTAP extended with state merging. We prove that MooreMI has the\nfundamental identification in the limit property. We also compare the\nalgorithms experimentally in terms of the size of the learned machine and\nseveral notions of accuracy, introduced in this paper. Finally, we compare with\nOSTIA, an algorithm that learns a more general class of transducers, and find\nthat OSTIA generally does not learn a Moore machine, even when fed with a\ncharacteristic sample.","url_abs":"http://arxiv.org/abs/1605.07805v2","url_pdf":"http://arxiv.org/pdf/1605.07805v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"learning-moore-machines-from-input-output","repo_url":"https://github.com/hwalinga/FSM-learning","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"learning-theory","task_name":"Learning Theory"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}