Content uploaded by Karlis Podnieks

Author content

All content in this area was uploaded by Karlis Podnieks on May 07, 2017

Content may be subject to copyright.

Duplicate of:
https://www.researchgate.net/publication/316715107_On_computation_in_the_limit_by_non-deterministic_Turing_machines

Content uploaded by Karlis Podnieks

Author content

All content in this area was uploaded by Karlis Podnieks on May 07, 2017

Content may be subject to copyright.

... The search for such a t is accomplished via dovetailing. Hence, if s is the sequence of outputs produced by the above algorithm on input x, then the belief level monotonically approaches 1 iff ψ(x) = u. 2 Theorem 3 is easily contrasted with the following: Theorem 4. (See R. Freivalds and K. Podnieks [6], in English see [8].) A function ψ is limit computable iff G ψ ∈ Σ 2 . ...

... The proof is completed by the observation that for an arbitrary choice set S for which A produces output sequence s on input x, E(s, u) converges to 1 iff ∀t ∃t > t Q(S, x, u, y, t). 2 Theorem 5 contrasts nicely with Theorem 6. (See R. Freivalds and K. Podnieks [6], in English see [10].) A function ψ is nondeterministically limit ...

We study learning of predicate logics formulas from “elementary facts,” i.e. from the values of the predicates in the given model.
Several models of learning are considered, but most of our attention is paid to learning with belief levels. We propose an axiom
system which describes what we consider to be a human scientist’s natural behavior when trying to explore these elementary facts.
It is proved that no such system can be complete. However we believe that our axiom system is “practically” complete. Theorems
presented in the paper in some sense confirm our hypothesis.

... Theorem 4. (See R. Freivalds and K. Podnieks [6] ...

... (See R. Freivalds and K. Podnieks [6] ...

We study learning of predicate logics formulas from "elementary facts," i.e. from the values of the predicates in the given model. Several models of learning are considered, but most of our attention is paid to learning with belief levels. We propose an axiom system which describes what we consider to be a human scientist's natural behavior when trying to explore these elementary facts. It is proved that no such system can be complete. However we believe that our axiom system is "practically" complete. Theorems presented in the paper in some sense confirm our hypothesis.

We study learning of predicate logics formulas from ''elementary facts,'' i.e. from the values of the predicates in the given model. Several models of learning are considered, but most of our attention is paid to learning with belief levels. We propose an axiom system which describes what we consider to be a human scientist's natural behavior when trying to explore these elementary facts. It is proved that no such system can be complete. However we believe that our axiom system is ''practically'' complete. Theorems presented in the paper in some sense confirm our hypothesis.

ResearchGate has not been able to resolve any references for this publication.