TY - GEN
T1 - Learning and Characterizing Fully-Ordered Lattice Automata
AU - Fisman, Dana
AU - Saadon, Sagi
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022/10/21
Y1 - 2022/10/21
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85142678211&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-19992-9_17
DO - 10.1007/978-3-031-19992-9_17
M3 - Conference contribution
SN - 9783031199912
VL - 13505
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 266
EP - 282
BT - Automated Technology for Verification and Analysis - 20th International Symposium, ATVA 2022, Proceedings
A2 - Bouajjani, Ahmed
A2 - Holík, Lukáš
A2 - Wu, Zhilin
PB - Springer
CY - Cham
T2 - 20th International Symposium on Automated Technology for Verification and Analysis, ATVA 2022
Y2 - 25 October 2022 through 28 October 2022
ER -