A Neurocognitive Approach to Robotic Cause-Effect Reasoning During Learning

Abstract

This project developed a purely neurocomputational cognitive architecture for robotic systems that are doing imitation learning (learning from demonstrations). The main results were: 1. implementation of a neural virtual machine; 2. implementation of a neural system that automatically creates explanations for a human demonstrators intentions/goals by using cause-effect reasoning; and 3. comparative analysis of human versus robot behavioral activities during imitation learning.

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Document Details

Document Type
Technical Report
Publication Date
May 23, 2022
Accession Number
AD1169707

Entities

People

  • Garrett E. Katz
  • James Reggia
  • Rodolphe Gentili

Organizations

  • Syracuse University
  • University of Maryland

Tags

DTIC Thesaurus Topics

  • Abstracts
  • Algorithms
  • Artificial Intelligence
  • Causal Reasoning
  • Cognitive Systems Engineering
  • Cognitive Workload
  • Computational Neuroscience
  • Computer Programming
  • Computer Science
  • Computers
  • Demonstrations
  • Human-Computer Interaction
  • Lisp Programming Language
  • Machine Learning
  • Maryland
  • Neural Networks
  • Reasoning
  • Simulations
  • Virtual Machines

Fields of Study

  • Computer science
  • Psychology

Readers

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

Technology Areas

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