An Evolutionary Approach to Learning in Robots.

Abstract

Evolutionary learning methods have been found to be useful in several areas in the development of intelligent robots. In the approach described here, evolutionary algorithms are used to explore alternative robot behaviors within a simulation model as a way of reducing the overall knowledge engineering effort. This paper presents some initial results of applying the SAMUEL genetic learning system to a collision avoidance and navigation task for mobile robots. (AN)

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1994
Accession Number
ADA294080

Entities

People

  • Alan Schultz
  • John Grefenstette

Organizations

  • United States Naval Research Laboratory

Tags

Communities of Interest

  • Autonomy
  • Sensors

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Autonomous Systems
  • Climate Change
  • Collision Avoidance
  • Computational Science
  • Computer Programming
  • Computers
  • Control Systems
  • Engineering
  • Evolutionary Algorithms
  • Genetic Algorithms
  • Intelligent Systems
  • Machine Learning
  • Neural Networks
  • Robotics
  • Robots

Fields of Study

  • Computer science

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Computational Modeling and Simulation

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Neural Networks
  • Autonomy
  • Biotechnology