Interactive Machine Learning Learning for Machine Training

Abstract

Machines that can learn new things from people who are not Machine Learning (ML) experts are of importance to the development of virtual training simulations and robotic teammates.We propose a research agenda framed around the human factors (HF) and ML research questions of teaching an agent via demonstration and critique. Ultimately, we will develop a training simulation game with several non- player characters, all of which can be easily taught new behaviors by an end-user.

Document Details

Document Type
DoD Grant Award
Publication Date
May 05, 2017
Source ID
N000141712373

Entities

People

  • Mark Riedl

Organizations

  • Georgia Tech Research Corporation
  • Office of Naval Research
  • United States Navy

Tags

Fields of Study

  • Computer science

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Instructional Design and Training Evaluation.
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks
  • Autonomy
  • Autonomy - Human-Robot Interaction