An Hybrid Expert Model to Support Tutoring Services in Robotic Arm Manipulations

  • Philippe Fournier-Viger
  • Roger Nkambou
  • André Mayers
  • Engelbert Mephu Nguifo
  • Usef Faghihi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7094)


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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  1. 1.
    Aleven, V., et al.: The Cognitive Tutor Authoring Tools (CTAT): Preliminary evaluation of efficiency gains. In: Ikeda, M., Ashley, K.D., Chan, T.-W. (eds.) ITS 2006. LNCS, vol. 4053, pp. 61–70. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  2. 2.
    Simon, H.A.: Information-processing theory of human problem solving. In: Estes, W.K. (ed.) Handbook of Learning and Cognitive Processes. Human information, vol. 5, pp. 271–295. John Wiley & Sons, Inc. (1978)Google Scholar
  3. 3.
    Lynch, C., Ashley, K., Aleven, V., Pinkwart, N.: Defining Ill-Defined Domains; A literature survey. In: Proc. Intelligent Tutoring Systems for Ill-Defined Domains Workshop at ITS 2006, pp. 1-10 (2006)Google Scholar
  4. 4.
    Mitrović, A., Mayo, M., Suraweera, P., Martin, B.: Constraint-Based Tutors: A Success Story. In: Monostori, L., Váncza, J., Ali, M. (eds.) IEA/AIE 2001. LNCS (LNAI), vol. 2070, pp. 931–940. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  5. 5.
    Clancey, W.: Use of MYCIN’s rules for tutoring. In: Buchanan, B., Shortliffe, E.H. (eds.) Rule-Based Expert Systems. Addison-Wesley (1984)Google Scholar
  6. 6.
    Graesser, A., Wiemer-Hastings, P., Wiemer-Hastings, K., Harter, D., Person, N.: Using Latent Semantic Analysis to evaluate the contributions of students in AutoTutor. Interactive Learning Environments 8, 149–169 (2000)CrossRefGoogle Scholar
  7. 7.
    Barnes, T., Stamper, J.: Toward Automatic Hint Generation for Logic Proof Tutoring Using Historical Student Data. In: Woolf, B.P., Aïmeur, E., Nkambou, R., Lajoie, S. (eds.) ITS 2008. LNCS, vol. 5091, pp. 373–382. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  8. 8.
    Fournier-Viger, P., Nkambou, R., MephuNguifo, E.: Learning Procedural Knowledge from User Solutions To Ill-Defined Tasks in a Simulated Robotic Manipulator. In: Romero, et al. (eds.) Handbook of Educational Data Mining, pp. 451–465. CRC Press (2010)Google Scholar
  9. 9.
    Belghith, K., Kabanza, F., Hartman, L.: Anytime Dynamic Path-planning with Flexible Probabilistic Roadmaps. In: Proc. 12th Intern. Conf. on Robotics and Automation (ICRA 2006), pp. 2372–2377 (2006)Google Scholar
  10. 10.
    Burgess, N.: Spatial memory: how egocentric and allocentric combine. Trends in Cognitive Sciences 10(12), 551–557 (2006)CrossRefGoogle Scholar
  11. 11.
    Nadel, L., Hardt, O.: The Spatial Brain. Neuropsychology 18(3), 473–476 (2004)CrossRefGoogle Scholar
  12. 12.
    Tversky, B.: Cognitive Maps, Cognitive Collages, and Spatial Mental Models. In: Campari, I., Frank, A.U. (eds.) COSIT 1993. LNCS, vol. 716, pp. 14–24. Springer, Heidelberg (1993)Google Scholar
  13. 13.
    Gunzelmann, G., Lyon, D.R.: Mechanisms for Human Spatial Competence. In: Barkowsky, T., Knauff, M., Ligozat, G., Montello, D.R. (eds.) Spatial Cognition 2007. LNCS (LNAI), vol. 4387, pp. 288–307. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  14. 14.
    Fournier-Viger, P., Nkambou, R., Mayers, A.: Evaluating Spatial Representations and Skills in a Simulator-Based Tutoring System. IEEE Transactions on Learning Technologies 1(1), 63–74 (2008)CrossRefGoogle Scholar
  15. 15.
    Koedinger, K.R., Anderson, J.R., Hadley, W.H., Mark, M.A.: Intelligent Tutoring goes to school in the big city. Intenational Journal of Artificial Intelligence in Education 8, 30–43 (1997)Google Scholar
  16. 16.
    Carruth, D., et al.: Symbolic Model of Perception in Dynamic 3D Environments. In: Proc. 25th Army Science Conf. (2006)Google Scholar
  17. 17.
    Agrawal, R., Srikant, R.: Mining Sequential Patterns. In: Proc. ICDE, pp. 3–14 (1995)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Philippe Fournier-Viger
    • 1
  • Roger Nkambou
    • 1
  • André Mayers
    • 2
  • Engelbert Mephu Nguifo
    • 3
  • Usef Faghihi
    • 4
  1. 1.Dept. of Computer SciencesUniversity of Quebec in MontrealCanada
  2. 2.Dept. of Computer SciencesUniversity of SherbrookeCanada
  3. 3.Dept. of Mathematics and Computer SciencesUniversité Blaise-Pascal Clermont 2France
  4. 4.Dept. of Computer SciencesUniversity of MemphisU.S.A.

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