Multi-paradigm generation of tutoring feedback in robotic arm manipulation training

  • Authors:

  • Philippe Fournier-Viger;Roger Nkambou;André Mayers;Engelbert Mephu-Nguifo;Usef Faghihi

  • Affiliations:

  • Dept. of Computer Sciences, University of Moncton, Canada;Dept. of Computer Sciences, University of Quebec, Montreal, Canada;Dept. of Computer Sciences, University of Sherbrooke, Canada;LIMOS, Clermont Université, Université Blaise Pascal, Clermont-Ferrand, France,LIMOS, CNRS, UMR 6158, Aubière, France;Dept. of Computer Sciences, University of Quebec, Montreal, Canada

  • Venue:
  • ITS'12 Proceedings of the 11th international conference on Intelligent Tutoring Systems
  • Year:
  • 2012

Quantified Score

Hi-index 0.00

Visualization

Abstract

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.