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| Content Provider | Springer Nature Link |
|---|---|
| Author | Castillo, Luis Morales, Lluvia González Ferrer, Arturo Fdez Olivares, Juan Borrajo, Daniel Onaindía, Eva |
| Copyright Year | 2009 |
| 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. |
| Starting Page | 347 |
| Ending Page | 362 |
| Page Count | 16 |
| File Format | |
| ISSN | 10946136 |
| Journal | Journal of Scheduling |
| Volume Number | 13 |
| Issue Number | 4 |
| e-ISSN | 10991425 |
| Language | English |
| Publisher | Springer US |
| Publisher Date | 2009-11-19 |
| Publisher Place | Boston |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Production/Logistics Artificial Intelligence (incl. Robotics) Optimization Calculus of Variations and Optimal Control; Optimization Operations Research/Decision Theory |
| Content Type | Text |
| Resource Type | Article |
| Subject | Artificial Intelligence Management Science and Operations Research Engineering Software |
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