Automatic generation of temporal planning domains for e-learning problems
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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.
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- Alguacil-Martin, S. (2008). Tailored learning experiences. Andalusian Regional Government Research Portal. www.andaluciainvestiga.com/espanol/noticias/9/6915.asp.
- ANSI/IEEE (2007). IEEE Standard for Learning Object Metadata. http://ltsc.ieee.org/wg12/.
- Boticario, J., & Santos, O. (2007). A dynamic assistance approach to support the development and modelling of adaptive learning scenarios based on educational standards. In Fifth international workshop on authoring of adaptive and adaptable hypermedia. International conference on user modelling. Google Scholar
- Castillo, L., Fdez-Olivares, J., García-Pérez, O., & Palao, F. (2006). Efficiently handling temporal knowledge in an HTN planner. In Sixteenth international conference on automated planning and scheduling, ICAPS. Google Scholar
- Cole, J., & Foster, H. (2007). Using moodle. Sebastopol: O’Reilly. Google Scholar
- Edelkamp, S., & Hoffmann, J. (2004). The language for the 2004 international planning competition. http://ls5-www.cs.uni-dortmund.de/~edelkamp/ipc-4/pddl.html.
- Fdez-Olivares, J., Castillo, L., García-Pérez, O., & Palao, F. (2006). Bringing users and planning technology together. Experiences in SIADEX. In Sixteenth international conference on automated planning and scheduling, ICAPS. Awarded as the Best Application Paper of this edition. Google Scholar
- Figueroa-Martínez, J., Morales, L., & Castillo, L. (2008). Extending moodle SCORM module to support basic ims-ld. In MoodleMoot, Spain, 2008. Google Scholar
- Ghallab, M., Nau, D., & Traverso, P. (2004). Automated planning: theory and practice. San Mateo: Morgan Kaufmann. Google Scholar
- Goldman, R. (2006). Durative planning in HTNs. In Proceedings of ICAPS. Google Scholar
- González-Ferrer, A., Castillo, L., Fdez-Olivares, J., & Morales, L. (2008). Towards the use of xpdl as planning and scheduling modeling tool: the workflow patterns approach. In Advances in artificial intelligence (IBERAMIA 2008). Berlin: Springer. Google Scholar
- Ilias, L. M. S. (2007). ILIAS website. http://www.ilias.de/ios/index-e.html.
- IMS-GLC (2007). IMS Global Learning Consortium. http://www.imsglobal.org/.
- Koper, R. (2005). An introduction to learning design. In R. Koper & C. Tattersall (Eds.), Learning design: a handbook on modelling and delivering networked education and training (pp. 3–20). Berlin: Springer. Google Scholar
- Kuter, U., Nau, D., Reisner, E., & Goldman, R. (2007). Conditionalization: adapting forward-chaining planners to partially observable environments. In ICAPS 2007—workshop on planning and execution for real-world systems. Google Scholar
- Long, D., & Fox, M. (2003). PDDL2.1: an extension to PDDL for expressing temporal planning domains. Journal of Artificial Intelligence Research, 20, 61–124. Google Scholar
- Luckin, R., Underwood, J., du Boulay, B., Holmberg, J., Kerawalla, L., O’Connor, J., Smith, H., & Tunley, H. (2006). Designing educational systems fit for use: a case study in the application of human centred design for AIED. International Journal of Artificial Intelligence in Education, 16, 353–380. Google Scholar
- Morales, L., Castillo, L., Fernández-Olivares, J., & González-Ferrer, A. (2008). Building learning designs by using an automatic planning domain generation: A state-based approach. In European starting AI researcher symposium, STAIRS08. Google Scholar
- Muscettola, N., Nayak, P. P., Pell, B., & Williams, B. C. (1998). Remote agent: to boldly go where no AI systems has gone before. Artificial Intelligence, 5–48. Google Scholar
- Myers, K. L. (1999). CPEF: A continuous planning and execution framework. AI Magazine, 20(4), 63–69. Google Scholar
- Nau, D., Au, T., Ilghami, O., Kuter, U., Murdock, J. W., Wu, D., & Yaman, F. (2003). SHOP2: An HTN planning system. Journal of Artificial Intelligence Research, 20, 379–404. Google Scholar
- Petrick, R., & Bacchus, F. (2004). Extending the knowledge-based approach to planning with incomplete information and sensing. In Proceedings of the international conference on automated planning and scheduling (pp. 2–11). Google Scholar
- R-Moreno, M., & Camacho, D. (2006). AI techniques for automatic learning design. In International electronic conference on computer science (IeCCS-2006). Google Scholar
- Sicilia, M. A., Sánchez-Alonso, S., & García-Barriocanal, E. (2006). On supporting the process of learning design through planners. In F. J. García et al. (Eds.), Virtual campus 2006 selected and extended papers (pp. 81–89). Google Scholar
- Toro, S. T., Castillo, L., Morales, L., Jiménez-Galera, M., Llorca-Díez, M., & Carrillo, J. O. (2008). Customizing learning routes in moodle based on Honey–Alonso learning styles. In MoodleMoot, Spain, 2008. Google Scholar
- Ullrich, C. (2005). Course generation based on HTN planning. In Proceedings of 13th annual workshop of the SIG adaptivity and user modeling in interactive systems (pp. 74–79). Google Scholar
- Wilkins, D. E., & des Jardins, M. (2001). A call for knowledge-based planning. AI Magazine, 22(1), 99–115. Google Scholar
- Wilkins, D. E., & Desimone, R. V. (1994). Applying an AI planner to military operations planning. In M. Zweben & M. S. Fox (Eds.), Intelligent scheduling. San Mateo: Morgan Kaufmann. Google Scholar