Author:
Gasarch William I.,Smith Carl H.
Abstract
Traditional work in inductive inference has been to model a learner receiving data about a function
f
and trying to learn the function. The data is usually just the values
f
(0),
f
(1),…. The scenario is modeled so that the learner is also allowed to ask questions about the data (e.g., (∀ χ) [χ> 17 →
f
(
χ
) = 0]?). An important parameter is the language that the lerner may use to formulate queries. We show that for most languages a learner can learn more by asking questions than by passively receiving data. Mathematical tools used include the solution to Hilbert's tenth problem, the decidability of Presuburger arithmetic, and ω-automata.
Publisher
Association for Computing Machinery (ACM)
Subject
Artificial Intelligence,Hardware and Architecture,Information Systems,Control and Systems Engineering,Software
Cited by
35 articles.
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