Polynomial-time DTMC model checking against FDFA ω-regular properties, plus FUFA model with single-exponential LTL translation and greater succinctness than FDFA or unambiguous Büchi automata.
Characterizing LTL Formulas by Examples
1 Pith paper cite this work. Polarity classification is still indexing.
abstract
We investigate the extent to which Linear Temporal Logic (LTL) formulas can be uniquely characterized by a finite set of labeled examples. We consider different types of examples, ranging from finite words to transfinite words, as well as schematic examples. In the finite-word setting, we provide a complete classification of basis-restricted LTL fragments that admit such unique characterizations. Next, we show that allowing transfinite words as examples enables finite unique characterizations for large monotone fragments of LTL. Finally, we introduce schematic examples, i.e., patterns that compactly represent a family of finite words, and we show that these enable unique characterization results in the finite setting that were not possible with ordinary finite examples alone. Overall, the work provides a foundational account of the descriptive power of different example types for example-driven specification, debugging, and learning of temporal properties.
fields
cs.FL 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
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Probabilistic Model Checking via Families of Deterministic and Unambiguous Finite Automata
Polynomial-time DTMC model checking against FDFA ω-regular properties, plus FUFA model with single-exponential LTL translation and greater succinctness than FDFA or unambiguous Büchi automata.