The Role of the Critic in Learning Systems,

Abstract

Buchanan, Mitchell, Smith, and Johnson (Buchanan 78a) described a general model of learning systems that included a component called the Critic. The task of the Critic was described as threefold: evaluation of the past actions of the performance element of the learning system, localization of credit and blame to particular portions of that performance element and recommendation of possible improvements and modifications in the performance element. This article analyzes these three tasks in detail and surveys the methods that have been employed in existing learning systems to accomplish them. The principle method used to evaluate the performance element is to develop a global performance standard by (a) consulting an external source of knowledge, (b) consulting an internal source of knowledge, or (c) conducting deep search. Credit and blame have been localized by (a) asking an external knowledge source to do the localization, (b) factoring the global performance standard to produce a local performance standard, and (c) conducting controlled experiments on the performance element. Recommendations have been communicated to the learning element using (a) local training instances, (b) correlation coefficients, and (c) partially-instantiated schemata. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1981
Accession Number
ADA113479

Entities

People

  • B. G. Buchanan
  • Thomas G. Dietterich

Organizations

  • Stanford University

Tags

Communities of Interest

  • Biomedical
  • Cyber
  • Human Systems

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Circuit Boards
  • Computer Science
  • Computers
  • Debugging
  • Expert Systems
  • Mass Spectra
  • Mass Spectrometers
  • New York
  • Pattern Recognition
  • Printed Circuits
  • Simulations
  • Simulators
  • Spectra
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  • Standards
  • Test Methods

Readers

  • Artificial Intelligence
  • Instructional Design and Training Evaluation.
  • Military History

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks