Multi-paradigm Generation of Tutoring Feedback in Robotic Arm Manipulation Training
Philippe Fournier-Viger, Roger Nkambou, André Mayers, Engelbert Mephu-Nguifo, Usef Faghihi
Building an intelligent tutoring system requires to define an expertise model that can support appropriate tutoring services. This is usually done by adopting one of the following paradigms: building a cognitive model, specifying constraints, integrating an expert system and using data mining algorithms to learn domain knowledge. However, for some ill-defined domains, the use of a single paradigm could lead to a weak support of the user in terms of tutoring feedback. To address, this issue, we propose to use a multi-paradigm approach. We illustrate this idea in a tutoring system for robotic arm manipulation training. To support tutoring services in this ill-defined domain, we have developed a multi-paradigm model combining: (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 indicate that the multi-paradigm approach allows providing assistance to learners that is much richer than what is offered with each single paradigm.
The final publication is available at Springer via https://doi.org/10.1007/978-3-642-30950-2_29.