ADL: exploring the middle ground between STRIPS and the situation calculus
Proceedings of the first international conference on Principles of knowledge representation and reasoning
The computational complexity of propositional STRIPS planning
Artificial Intelligence
Integrated E-Learning: Pedagogy, Technology and Organization
Integrated E-Learning: Pedagogy, Technology and Organization
Automated Planning: Theory & Practice
Automated Planning: Theory & Practice
Traveling Salesman Problems with Profits
Transportation Science
Pedagogically founded courseware generation based on HTN-planning
Expert Systems with Applications: An International Journal
The metric-FF planning system: translating "Ignoring delete lists" to numeric state variables
Journal of Artificial Intelligence Research
Planning with goal utility dependencies
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Automatic generation of temporal planning domains for e-learning problems
Journal of Scheduling
A look-ahead B&B search for cost-based planning
CAEPIA'09 Proceedings of the Current topics in artificial intelligence, and 13th conference on Spanish association for artificial intelligence
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The automatic generation of individualized plans in specific domains is an open problem that combines aspects related to automated planning, machine learning or recommendation systems technology. In this paper, we focus on a specific instance of that task; that of generating e-learning courses adapted to students' profiles, within the automated planning paradigm. One of the open problems in this type of automated planning application relates to what is known as oversubscription: given a set of goals, each one with a utility, obtain a plan that achieves some (or all) the goals, maximizing the utility, as well as minimizing the cost of achieving those goals. In the generation of e-learning designs there is only one goal: generating a course design for a given student. However, in order to achieve the goal, the course design can include many different kinds of activities, each one with a utility (that depend on the student profile) and cost. Furthermore, these activities are usually grouped into clusters, so that at least one of the activities in each cluster is needed, though many more can be used. Finally, there is also an overall cost threshold (usually in terms of student time). In this paper, we present our work on building an individualized e-learning design. We pose each course design as a variation of the oversubscription problem, that we call the clustered-oversubscription problem, and we use linear programming for assisting a planner to generate the design that better adapts to the student.