Classifying different criteria for learning algebraic structures Full article
Journal |
Annals of Pure and Applied Logic
ISSN: 0168-0072 |
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Output data | Year: 2026, Volume: 177, Number: 1, Pages: 103648 Pages count : 25 DOI: 10.1016/j.apal.2025.103648 | ||||||||||||
Tags | Inductive inference, Algorithmic learning theory, Infinitary logic, Continuous reducibility | ||||||||||||
Authors |
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Affiliations |
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Funding (1)
1 | Russian Science Foundation | 24-11-00227 |
Abstract:
In the last years there has been a growing interest in the study of learning problems associated with algebraic structures. The framework we use models the scenario in which a learner is given larger and larger fragments of a structure from a given target family and is required to output an hypothesis about the structure’s isomorphism type. So far researchers focused on Ex-learning, in which the learner is asked to eventually stabilize to the correct hypothesis, and on restrictions where the learner is allowed to change the hypothesis a fixed number of times. Yet, other learning paradigms coming from classical algorithmic learning theory remained unexplored. We study the "learning power'' of such criteria, comparing them via descriptive-settheoretic tools thanks to the novel notion of E-learnability. The main outcome of this paper is that such criteria admit natural syntactic characterizations in terms of infinitary formulas analogous to the one given for Ex-learning in [8]. Such characterizations give a powerful method to understand whether a family of structures is learnable with respect to the desired criterion.
Cite:
Bazhenov N.
, Cipriani V.
, Jain S.
, San Mauro L.
, Stephan F.
Classifying different criteria for learning algebraic structures
Annals of Pure and Applied Logic. 2026. V.177. N1. P.103648. DOI: 10.1016/j.apal.2025.103648 WOS Scopus OpenAlex
Classifying different criteria for learning algebraic structures
Annals of Pure and Applied Logic. 2026. V.177. N1. P.103648. DOI: 10.1016/j.apal.2025.103648 WOS Scopus OpenAlex
Dates:
Submitted: | Sep 18, 2024 |
Accepted: | Aug 18, 2025 |
Published online: | Aug 22, 2025 |
Published print: | Aug 27, 2025 |
Identifiers:
Web of science: | WOS:001562374100001 |
Scopus: | 2-s2.0-105014651632 |
OpenAlex: | W4413427393 |
Citing:
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