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.
Logi- cal Methods in Computer ScienceV olume 19, Issue 2, 5 (Apr 2023)
2 Pith papers cite this work. Polarity classification is still indexing.
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AL*RTA learns alternating real-time automata, terminates, and produces smaller models than NL*RTA at the expense of more queries while preserving expressive power.
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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.
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Learning Alternating Real-Time Automata
AL*RTA learns alternating real-time automata, terminates, and produces smaller models than NL*RTA at the expense of more queries while preserving expressive power.