Syntactic characterization of learnability of structures with mind changes
Year: 2025
Type: article
Abstract: We study the learnability of classes of computable structures under models that allow finitely many mind changes. Extending classical notions of explanatory learning from informant and from text, we introduce new paradigms where the information source and the convergence requirements are modified. In particular, we define Δ 2 0 -learning, where each atomic fact may be presented with finitely many errors before stabilizing, and c.e.- and d.c.e.-learning , where information is restricted to positive atomic facts that may either never be retracted (c.e.) or be retracted at most once (d.c.e.). We provide syntactic characterizations for these notions: Δ 2 0 -learning coincides with definability by finite existential sentences, and c.e.-learning coincides with TxtEx-learning. For d.c.e.-learning we give partial characterization in restricted languages. Furthermore, for n -learning from informant, where learners are allowed at most n mind changes, we establish a complete syntactic characterization in terms of specific infinitary formulas of bounded depth.
Source: Information and Computation
Authors: Ekaterina B. Fokina, Steffen Lempp
Institutions: TU Wien, University of Wisconsin–Madison
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $2,840
Award: G7892346488
OpenAlex ID: W4416345402