Asymptotic Hausdorff and Language Similarity

We introduce the Asymptotic Hausdorff lifting, denoted Aℍ_d, a general method for lifting an element-level metric d to a (pseudo-) metric on sets, that captures asymptotic similarity in infinite domains equipped with a notion of size. The construction is designed to be insensitive to finite deviations and to avoid the limitations of classical Hausdorff-based approaches, which are often overly sensitive to outliers and fail to reflect asymptotic behavior. Formal languages provide a central motivating instance of this framework, where elements are words and sets are languages. When applied to normalized edit distances, the Asymptotic Hausdorff lifting yields metric-valued distances between languages that reflect asymptotic edit behavior while preserving metric structure. We study the equivalence classes of regular languages induced by Aℍ_d for normalized edit distances d, and characterize their asymptotic essence. Focusing in particular on the normalized edit distance of Marzal and Vidal, ned, we investigate the computation of Aℍ_ned for regular languages and for bounded context-free languages.