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- Title
A NOTE ON THE LEARNING-THEORETIC CHARACTERIZATIONS OF RANDOMNESS AND CONVERGENCE.
- Authors
STEIFER, TOMASZ
- Abstract
Recently, a connection has been established between two branches of computability theory, namely between algorithmic randomness and algorithmic learning theory. Learning-theoretical characterizations of several notions of randomness were discovered. We study such characterizations based on the asymptotic density of positive answers. In particular, this note provides a new learning-theoretic definition of weak 2-randomness, solving the problem posed by (Zaffora Blando, Rev. Symb. Log. 2019). The note also highlights the close connection between these characterizations and the problem of convergence on random sequences.
- Subjects
ALGORITHMIC randomness; COMPUTABLE functions; RANDOM variables; PROBLEM solving; KOLMOGOROV complexity
- Publication
Review of Symbolic Logic, 2022, Vol 15, Issue 3, p807
- ISSN
1755-0203
- Publication type
Article
- DOI
10.1017/S1755020321000125