Learning Canonical Register Automata over Ordered Data Domains

Abstract:Register automata are finite automata equipped with memory that recognize data languages over infinite alphabets. In this work, we investigate active learning algorithms for deterministic register automata (DRAs) over ordered data domains--covering both dense domains, such as the rationals, and non-dense domains such as the integers. We show that the active learning problem for DRAs over both dense and non-dense ordered domains can be treated within a single unified framework. More specifically, we develop and implement a polynomial-time active learning procedure for DRAs over ordered domains, using oracles for membership, equivalence and memorability queries. The memorability queries were originally introduced for learning DRAs over domains with identity tests. Our unified framework also leads to a new consequence: minimization of DRAs over the non-dense ordered domain of integers is decidable, extending a result previously known only for dense domains. Finally, we give improved complexity bounds of several decision problems for DRAs over ordered domains that are closely related to the queries used in active learning.

Computation and Language (cs.CL); Machine Learning (cs.LG)

arXiv:2608.18765 [cs.CL]

(or arXiv:2608.18765v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2608.18765 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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