Neuro-Symbolic Learning for Context-Aware Real-Time Human-Agent Interaction

Abstract

Robotic agents have been developed to perform a number of tasks under human command and sometimes independently as well. Human-agent teams can extend the capabilities of both humans and robots. Multi-Agent Reinforcement Learning (MARL) is a natural approach for such teams. The interaction within a team composed of human and robot agents considered so far ignores the role of physical context in MARL. Here, we present a distributed online MARL in such a scenario incorporating wireless communication and physical movement supplemented with intra-team communication. We present the system, its mathematical analysis, and some initial experimental results herein

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 2024
Accession Number
AD1230960

Entities

People

  • Julian De Gortari Briseno
  • Mani Srivastava
  • Vinod K. Mishra

Organizations

  • United States Army Research Laboratory
  • University of California

Tags

Fields of Study

  • Computer science

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.

Technology Areas

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