Flexible Knowledge Acquisition Through Explicit Representation of Knowledge Roles

Abstract

A system that acquires knowledge from a user should be able to reflect upon the knowledge that it has at each moment and understand what kinds of new knowledge it needs to learn. For the past two decades, research in the area of knowledge acquisition has been moving towards systems that have access to richer representations of knowledge about their task. This paper reviews some well-known knowledge acquisition tools representative of this trend. It also describes our recent work in EXPECT, a system with explicit representations of knowledge about the task and the domain that supports knowledge acquisition for a wider range of tasks and applications than its predecessors. We hope our observations will be useful to researchers in user interfaces and in machine learning concerned with acquiring information from users.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1996
Accession Number
ADA459767

Entities

People

  • Bill Swartout
  • Yolanda Gil

Organizations

  • University of Southern California

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Abstracts
  • Acquisition
  • Artificial Intelligence
  • Artificial Intelligence Computing
  • Classification
  • Computer Languages
  • Construction
  • Expert Systems
  • Guidance
  • Information Science
  • Knowledge Based Systems
  • Language
  • Machine Learning
  • Military Transportation
  • Models
  • Ontologies
  • Transportation

Readers

  • Computational Modeling and Simulation
  • Geospatial Intelligence and Artificial Intelligence Analytics
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - DoD AI Strategy