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New framework simplifies learning of register automata over ordered data domains

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a unified framework for actively learning deterministic register automata (DRAs) over ordered data domains, including both dense and non-dense sets like rationals and integers. This new procedure operates in polynomial time and utilizes oracles for membership, equivalence, and memorability queries. A significant outcome of this work is the decidability of minimizing DRAs over the non-dense ordered domain of integers, a problem previously only known to be solvable for dense domains. The research also provides enhanced complexity bounds for related decision problems concerning DRAs. AI

IMPACT Introduces a more efficient method for learning complex data structures relevant to AI, potentially improving pattern recognition and data analysis capabilities.

RANK_REASON The cluster contains a single academic paper detailing a new algorithmic framework for learning a specific type of automata. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New framework simplifies learning of register automata over ordered data domains

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The cluster contains a single academic paper detailing a new algorithmic framework for learning a specific type of automata. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Yong Li, Qiyi Tang, Di-De Yen ·

    Learning Canonical Register Automata over Ordered Data Domains

    arXiv:2608.18765v1 Announce Type: new 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 d…

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