Grammatical Inference as a Satisfiability Modulo Theories Problem
Formal Languages and Automata Theory
2017-05-31 v1 Machine Learning
Logic in Computer Science
Abstract
The problem of learning a minimal consistent model from a set of labeled sequences of symbols is addressed from a satisfiability modulo theories perspective. We present two encodings for deterministic finite automata and extend one of these for Moore and Mealy machines. Our experimental results show that these encodings improve upon the state-of-the-art, and are useful in practice for learning small models.
Cite
@article{arxiv.1705.10639,
title = {Grammatical Inference as a Satisfiability Modulo Theories Problem},
author = {Rick Smetsers},
journal= {arXiv preprint arXiv:1705.10639},
year = {2017}
}
Comments
Submitted and selected for oral presentation at the LearnAut workshop at LICS 2017