Automatic generation of temporal planning domains for e-learning problems

Authors:
Luis Castillo;Lluvia Morales;Arturo González-Ferrer;Juan Fdez-Olivares;Daniel Borrajo;Eva Onaindía
Affiliations:
Departamento de Ciencias de la Computación e I.A., ETSI Informática y Telecomunicaciones, Universidad de Granada, Granada, Spain 18071;Departamento de Ciencias de la Computación e I.A., ETSI Informática y Telecomunicaciones, Universidad de Granada, Granada, Spain 18071;Center for Virtual Teaching, E-Learning Center, University of Granada, Granada, Spain;Departamento de Ciencias de la Computación e I.A., ETSI Informática y Telecomunicaciones, Universidad de Granada, Granada, Spain 18071;Departamento de Informática, Universidad Carlos III de Madrid, Leganés, Spain;Departamento de Sistemas Informáticos y Computación, Universidad Politécnica de Valencia, Valencia, Spain
Venue:
Journal of Scheduling
Year:
2010

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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.