Learning from Different Teachers

  • Authors:

  • Dana Angluin;Mārtiņš Kriķis

  • Affiliations:

  • Computer Science Department, Yale University, P.O. Box 208285, New Haven, CT 06520-8285, USA. dana.angluin@yale.edu;Intel Corporation, 75 Reed Rd., Hudson, MA 01749, USA. martins.krikis@intel.com

  • Venue:
  • Machine Learning
  • Year:
  • 2003

Quantified Score

Hi-index 0.00

Visualization

Abstract

We introduce a new model of a learner learning an unknown concept from examples with a teacher's help. In such models, “outright coding” refers to a situation in which the teacher sends the learner a representation of the concept, either directly or encoded via the examples. Previous models have used adversarial learners or adversarial teachers to try to prevent outright coding. Our model is an attempt to reflect more directly some of the reasons that outright coding is not a common mode of human learning.We model the learner as a Turing machine with oracle access to another programming system, called its “function box.” The programming system in its function box is initially unknown to the learner. The target concept is a partial recursive function and the goal of the learner is to find in its function box a function that is equal to or extends the target concept. We exhibit a class of learner/teacher pairs in which the learner can learn any partial recursive function, provided that the learner's function box is “not too much slower” than the teacher's. This result is shown not to hold if the learner's function box can contain an arbitrary acceptable programming system.