Accession Number:

ADA210593

Title:

On Learning from Exercises

Descriptive Note:

Technical rept.

Corporate Author:

CARNEGIE-MELLON UNIV PITTSBURGH PA ROBOTICS INST

Personal Author(s):

Report Date:

1989-02-01

Pagination or Media Count:

20.0

Abstract:

This paper explores a new direction in the formal theory of learning- -learning in the sense of improving computational efficiency as opposed to concept learning in the sense of Valient. Specifically, the paper concerns algorithms that learn to solve problems from sample instances of the problems. We develop a general framework for such learning and study the framework over two distinct random sources of sample instances. The first source provides sample instances together with their solutions, while the second source provides unsolved instances or exercises. We prove two theorems identifying conditions sufficient for learning over the two sources, our proofs being constructive in that they exhibit learning algorithms. To illustrate the scope of our results, we discuss their application to a program that learns to solve restricted classes of symbolic integrals.

Subject Categories:

  • Psychology
  • Numerical Mathematics

Distribution Statement:

APPROVED FOR PUBLIC RELEASE