{"paper":{"title":"Learn with SAT to Minimize B\\\"uchi Automata","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.FL","authors_text":"Martin Hofmann, Stephan Barth","submitted_at":"2012-10-09T00:53:36Z","abstract_excerpt":"We describe a minimization procedure for nondeterministic B\\\"uchi automata (NBA). For an automaton A another automaton A_min with the minimal number of states  is learned with the help of a SAT-solver.\n  This is done by successively computing automata A' that approximate A in the  sense that they accept a given finite set of positive examples and reject a  given finite set of negative examples. In the course of the procedure these example sets are successively increased. Thus, our method can be seen as an  instance of a generic learning algorithm based on a \"minimally adequate  teacher\" in the"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1210.2452","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}