Traditional automata classify words from a given alphabet as either good or bad. In many scenarios, in particular in formal verification, a finer classification is required. Fully-ordered lattice automata (FOLA) associate with every possible word a value from a finite set of values such as { 0, 1, 2, …, k}. In this paper we are interested in learning formal series that can be represented by FOLA. Such a series can be learned by a straight forward extension of the L∗ algorithm. However, this approach does not take advantage of the special structure of a FOLA. In this paper we investigate FOLAs and provide a Myhill-Nerode characterization for FOLAs, which serves as a basis for providing a specialized algorithm for FOLAs, which we term FOL∗. We compare the performance of FOL∗ to that of L∗ on synthetically generated FOLA. Our experiments show that FOL∗ outperforms L∗ in the number of states of the obtained FOLA, the number of issued value queries (the extension of membership queries to the quantitative setting), and the number of issued equivalence queries.
CITATION STYLE
Fisman, D., & Saadon, S. (2022). Learning and Characterizing Fully-Ordered Lattice Automata. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 13505 LNCS, pp. 266–282). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-19992-9_17
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