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Learning DFSMs from single string prefixes is NP-hard

By PulseAugur Editorial · [1 sources] ·

A new research paper published on arXiv details the computational complexity of learning Deterministic Finite-State Machines (DFSMs). The study focuses on scenarios where the input data is limited to prefixes of a single string. The findings indicate that approximating the minimum DFSM in such cases is NP-hard, even when the input consists solely of prefixes from one binary string. This complexity extends to both Moore and Mealy machines. AI

IMPACT This research contributes to the theoretical understanding of machine learning algorithms, specifically in the domain of formal language theory and automata, which underpins some AI approaches.

RANK_REASON Academic paper detailing computational complexity of a theoretical computer science problem. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.LG →

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

Learning DFSMs from single string prefixes is NP-hard

COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Radu Cosmin Dumitru, Ryo Yoshinaka, Ayumi Shinohara ·

    Learning Deterministic Finite-State Machines from the Prefixes of a Single String is NP-Complete

    arXiv:2601.12621v2 Announce Type: replace-cross Abstract: It is well known that computing a minimum deterministic finite automaton consistent with a given set of positive and negative examples is NP-hard. Previous work has identified conditions on the input sample under which the…

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