Finite Memory Estimation and Control of Finite Probabilistic Systems.

Abstract

This dissertation introduces concepts and associated computational procedures that are applicable to a mathematical problem arising in the context of Operations Research and Stochastic Control. Briefly stated, the problem is to design a strategy for real-time decision-making on the basis of imperfect (state) information and finite memory. The plant (i.e. the object to be controlled) is modelled as a finite probabilistic system (FPS) or stationary discrete-time finite-input finite-output finite-state controlled stochastic process, a generalization of the partially-observed Markov decision model initiated by Drake (1962), which itself generalizes the Markov decision model of Bellman (1957a).

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1977
Accession Number
ADA038813

Entities

People

  • Loren Kerry Platzman

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • C4I
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Automata
  • Computational Science
  • Computations
  • Computer Communications
  • Computer Programming
  • Computer Programs
  • Computers
  • Dynamic Programming
  • Electrical Engineering
  • Engineering
  • Linear Systems
  • Markov Processes
  • Operations Research
  • Probabilistic Models
  • Probability
  • Random Variables
  • Stochastic Processes

Fields of Study

  • Mathematics

Readers

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