DESIGN OF A LEARNING MACHINE AND THE STUDY OF SOME OF ITS CONVERGENCE CHARACTERISTICS.

Abstract

Whenever the dynamics and environment of a process are unknown or very complex, there is a need for 'learning machines' capable of learning the optimal decision algorithm from experience. This dissertation proposes such a learning machine. The basic learning situation is specified by a set of six postulates and the machine MAXINE is developed to learn in this situation. MAXINE is designed to have some of the qualities of human decision making: while being able to 'change its mind' in the face of new evidence, it is reluctant to alter firmly held opinions. The learning ability of this machine is tested by placing it in situations of varying degrees of complexity, including those which are deterministic and stochastic. Convergence of the proposed learning algorithm for the deterministic case is proved. (Author)

Document Details

Document Type
Technical Report
Publication Date
Sep 18, 1969
Accession Number
AD0694094

Entities

People

  • Robert Gordon Bellaire

Organizations

  • University of Washington

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Convergence
  • Dynamics
  • Environment
  • Learning
  • Learning Machines
  • Theses

Fields of Study

  • Computer science

Readers

  • Neural Network Machine Learning.
  • Systems Analysis and Design
  • Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.