None of the four properties in the refined A-translation admits a recursive characterization; the framework is extended with conjunction and a Rust prover is presented as a case study.
Cambridge University Press, July 2000.isbn: 9781139168717
2 Pith papers cite this work. Polarity classification is still indexing.
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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.
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On the Limits of Recursive Characterizations in the Refined $A$-Translation
None of the four properties in the refined A-translation admits a recursive characterization; the framework is extended with conjunction and a Rust prover is presented as a case study.
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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.