Adaptive Multi-Robot Behavior via Learning Momentum

Abstract

In this paper the effects of adaptive robotic behavior via Learning Momentum in the context of a robotic team are studied. Learning momentum is a variation on parametric adjustment methods that has previously been successfully applied to enhance individual robot performance. In particular we now assess. via simulation. the potential advantages of a team of robots using this capability to alter behavioral parameters when compared to a similar team of robots with static parameters.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2003
Accession Number
ADA443160

Entities

People

  • J. B. Lee
  • Ronald C. Arkin

Organizations

  • Georgia Tech

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Abstracts
  • Autonomous Navigation
  • Environment
  • Information Operations
  • Interception
  • Learning
  • Machine Learning
  • Momentum
  • Multiagent Systems
  • Navigation
  • Random Walk
  • Reinforcement Learning
  • Robot Navigation
  • Robots
  • Simulations
  • Software Design
  • Standards

Readers

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

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - Bayesian Inference
  • Autonomy
  • Autonomy - Autonomous System Control