Automatic Generation of Temporal Planning Domains for e-Learning Problems Articles uri icon

authors

  • CASTILLO, LUIS
  • MORALES, LLUVIA
  • GONZALEZ FERRER, ARTURO
  • FERNANDEZ OLIVARES, J.
  • BORRAJO MILLAN, DANIEL
  • ONAINDIA, E.

publication date

  • August 2010

start page

  • 347

end page

  • 362

issue

  • 4

volume

  • 13

international standard serial number (ISSN)

  • 1094-6136

electronic international standard serial number (EISSN)

  • 1099-1425

abstract

  • AI Planning & Scheduling techniques are being widely used to adapt learning paths to the special features and needs of students both in distance learning and lifelong learning environments. However,
    instructors strongly rely on Planning & Scheduling experts to encode
    and review the domains for the planner/scheduler to work. This paper
    presents an approach to automatically extract a fully operational HTN
    planning domain and problem from a learning objects repository without
    requiring the intervention of any planning expert, and thus enabling an
    easier adoption of this technology in practice. The results of a real
    experiment with a small group of students within an e-Learning private
    company in Spain are also shown.