Improving Tactical Plans with Genetic Algorithms,

Abstract

The problem of learning decision rules for sequential tasks is addressed, focusing on the problem of learning tactical plans from a simple flight simulator where a plane must avoid a missile. The learning method relies on the notion of competition and employs genetic algorithms to search the space of decision policies. In the research presented here, the use of available heuristic domain knowledge to initialize the population to produce better plans is investigated. (AN)

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1990
Accession Number
ADA294062

Entities

People

  • Alan C. Schultz
  • John J. Grefenstette

Organizations

  • United States Naval Research Laboratory

Tags

Communities of Interest

  • Air Platforms
  • Autonomy
  • Weapons Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Civil Engineering
  • Computer Languages
  • Computer Science
  • Demographic Cohorts
  • Detectors
  • Engineering
  • Environment
  • Flight Simulators
  • Genetic Algorithms
  • Information Science
  • Language
  • Learning
  • Machine Learning
  • Simulations
  • Simulators

Readers

  • Artificial Intelligence
  • Aviation Science / Aeronautics.
  • Computational Modeling and Simulation

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - DoD AI Strategy
  • AI & ML - Machine Learning Algorithms
  • Biotechnology
  • Space
  • Space - Space Objects