Open-World Learning for Radically Autonomous Agents

Abstract

In this paper, we pose a new research challenge to develop intelligent agents that exhibit radical autonomy by responding to sudden, long-term changes in their environments. We illustrate this idea with examples, identify abilities that support it, and argue that, although each ability has been studied in isolation, they have not been combined into integrated systems. We propose a framework for characterizing environments in which goal-directed physical agents operate, as well as ways those environments can change. We close by outlining approaches to the empirical study of such open-world learning.

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Document Details

Document Type
Technical Report
Publication Date
Nov 01, 2019
Accession Number
AD1122229

Entities

People

  • Pat Langley

Organizations

  • Institute for Defense Analyses

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Abstracts
  • Artificial Intelligence
  • Artificial Intelligence Computing
  • Artificial Intelligence Software
  • Autonomous Agents
  • Autonomous Systems
  • Autonomy
  • Change Detection
  • Chemical Reactions
  • Climate Change
  • Convolutional Neural Networks
  • Detection
  • Distance Learning
  • Environment
  • Information Systems
  • Integrated Systems
  • Intelligent Agents
  • Learning
  • Machine Learning
  • Mental Processes
  • Monitoring
  • Neural Networks
  • Psychology
  • Standards
  • Vehicles

Fields of Study

  • Computer science

Readers

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