Using planning techniques in intelligent tutoring systems
International Journal of Man-Machine Studies
Artificial Intelligence - Special issue on heuristic search in artificial intelligence
ITS '92 Proceedings of the Second International Conference on Intelligent Tutoring Systems
Branching and pruning: an optimal temporal POCL planner based on constraint programming
Artificial Intelligence
Planning graph as a (dynamic) CSP: exploiting EBL, DDB and other CSP search techniques in Graphplan
Journal of Artificial Intelligence Research
The FF planning system: fast plan generation through heuristic search
Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research
Engineering Applications of Artificial Intelligence
Engineering Applications of Artificial Intelligence
Automatic generation of temporal planning domains for e-learning problems
Journal of Scheduling
On the application of planning and scheduling techniques to e-learning
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part I
Automated instructional design for CSCL: A hierarchical task network planning approach
Expert Systems with Applications: An International Journal
Training for crisis decision making - An approach based on plan adaptation
Knowledge-Based Systems
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AI planning techniques offer very appealing possibilities for their application to e-learning environments. After all, dealing with course designs, learning routes and tasks keeps a strong resemblance with a planning process and its main components aimed at finding which tasks must be done and when. This paper focuses on planning learning routes under a very expressive constraint programming approach for planning. After presenting the general planning formulation based on constraint programming, we adapt it to an e-learning setting. This requires to model learners profiles, learning concepts, how tasks attain concepts at different competence levels, synchronisation constraints for working-group tasks, capacity resource constraints, multi-criteria optimisation, breaking symmetry problems and designing particular heuristics. Finally, we also present a simple example (modelled by means of an authoring tool that we are currently implementing) which shows the applicability of this model, the use of different optimisation metrics, heuristics and how the resulting learning routes can be easily generated.