SAI extends the RPNI framework with a splitting operation to identify symbolic automata over monotonic algebras with predicates a <= x < b in the limit, with a proof of polynomial-size characteristic samples.
World Scientific (01 1992)
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.FL 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
An L#-inspired active learning algorithm learns minimal separating DFAs for disjoint languages when one exists and outperforms prior methods on random and industrial benchmarks.
citing papers explorer
-
Passive Learning of Symbolic Automata over Monotonic Algebras
SAI extends the RPNI framework with a splitting operation to identify symbolic automata over monotonic algebras with predicates a <= x < b in the limit, with a proof of polynomial-size characteristic samples.
-
An $L^{\#}$ Based Algorithm for Active Learning of Minimal Separating Automata
An L#-inspired active learning algorithm learns minimal separating DFAs for disjoint languages when one exists and outperforms prior methods on random and industrial benchmarks.