Training Coordination Proxy Agents

Abstract

Delegating the coordination role to proxy agents can improve the overall outcome of the task at the expense of cognitive overload due to switching subtasks. Stability and commitment are characteristics of human teamwork but must not prevent the detection of better opportunities. In addition, coordination proxy agents must be trained from examples as a single agent but must interact with multiple agents. We apply machine learning techniques to the task of learning team preferences from mixed-initiative interactions and compare the outcome results of different simulated user patterns. This paper introduces a novel approach for the adjustable autonomy of coordination proxies based on the reinforcement learning of abstract actions.

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 2006
Accession Number
ADA466512

Entities

People

  • Myriam Abramson
  • Ranjeev Mittu
  • William Chao

Organizations

  • United States Naval Research Laboratory

Tags

Communities of Interest

  • Autonomy
  • Human Systems

DTIC Thesaurus Topics

  • Abstracts
  • Agent-Based Simulations
  • Aircrafts
  • Algorithms
  • Computational Science
  • Data Science
  • Grids
  • Information Science
  • Military Research
  • Models
  • Multiagent Systems
  • Pattern Recognition
  • Precision
  • Prototypes
  • Reinforcement Learning
  • Self Organizing Systems
  • Training

Fields of Study

  • Computer science

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Neural Network Machine Learning.
  • Strategic Security Studies

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - Neural Networks