Learning Sequential Decision Rules, Using Simulation Models and Competition,

Abstract

The problem of learning decision rules for sequential tasks is addressed, focusing on the problem of learning tactical decision rules from a simple flight simulator. The learning method relies on the notion of competition and employs genetic algorithms to search the space of decision policies. Several experiments are presented that address issues arising from differences between the simulation model on which learning occurs and the target environment on which the decision rules are ultimately tested. (AN)

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Document Details

Document Type
Technical Report
Publication Date
Mar 23, 1990
Accession Number
ADA294066

Entities

People

  • Alan C. Schultz
  • Connie L. Ramsey
  • John J. Grefenstette

Organizations

  • United States Naval Research Laboratory

Tags

Communities of Interest

  • Autonomy
  • Materials and Manufacturing Processes
  • Sensors
  • Weapons Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Computational Science
  • Computer Programming
  • Computer Programs
  • Computer Science
  • Computers
  • Detectors
  • Electrical Engineering
  • Engineering
  • Genetic Algorithms
  • Language
  • Machine Learning
  • Mathematical Models
  • Simulations
  • Simulators
  • Training

Fields of Study

  • Computer science

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • Biotechnology
  • Space
  • Space - Space Objects