Abstract
To build an intelligent tutoring system, a key task is to define an expertise model that can support appropriate tutoring services. However, for some ill-defined domains, classical approaches for representing expertise do not work well. To address this issue, we illustrate in this paper a novel approach which is to combine several approaches into a hybrid model to support tutoring services in procedural and ill-defined domains. We illustrate this idea in a tutoring system for operating Canadarm2, a robotic arm installed on the international space station. To support tutoring services in this ill-defined domain, we have developed a model combining three approaches: (1) a data mining approach for automatically building a task model from user solutions, (2) a cognitive model to cover well-defined parts of the task and spatial reasoning, (3) and a 3D path-planner to cover all other aspects of the task. Experimental results show that the hybrid model allows providing assistance to learners that is much richer than what could be offered by each individual approach.
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Fournier-Viger, P., Nkambou, R., Mayers, A., Nguifo, E.M., Faghihi, U. (2011). An Hybrid Expert Model to Support Tutoring Services in Robotic Arm Manipulations. In: Batyrshin, I., Sidorov, G. (eds) Advances in Artificial Intelligence. MICAI 2011. Lecture Notes in Computer Science(), vol 7094. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25324-9_41
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DOI: https://doi.org/10.1007/978-3-642-25324-9_41
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