The Application of an Absorbing Markov Chain in Prediction Learning

Abstract

A method is presented that utilizes the theory of Markov chains in predicting learning for manual activities. The model is applied to a realistic example and the results compared with learning curve theory. The results illustrate that a Markov chain approach to learning can give a good approximation to a real life situation. Recommendations are made for further applications of this model to actual situations, such as production lines. Areas of additional research are also discussed.

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Document Details

Document Type
Technical Report
Publication Date
May 01, 1969
Accession Number
AD0738947

Entities

People

  • William Jr Kanne

Organizations

  • United States Army Materiel Command

Tags

Communities of Interest

  • Energy and Power Technologies
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Aircraft Industry
  • Assembly
  • Assembly Lines
  • Classification
  • Computer Programs
  • Computers
  • Contracts
  • Engineering
  • Engineers
  • Industrial Engineering
  • Manufacturing
  • Markov Chains
  • Markov Processes
  • Probability
  • Production
  • Steady State
  • Stochastic Processes

Readers

  • Artificial Intelligence
  • Computational Modeling and Simulation
  • Mathematical Modeling and Probability Theory.