A Cognitive Tutoring Agent with Episodic and Causal Learning Capabilities

  • Usef Faghihi
  • Philippe Fournier-Viger
  • Roger Nkambou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6738)

Abstract

To mimic human tutor and provide optimal training, an intelligent tutoring agent should be able to continuously learn from its interactions with learners. Up to now, the learning capabilities of tutoring agents in educational systems have been generally very limited. In this paper, we address this issue with CELTS, a cognitive tutoring agent, whose architecture is inspired by the latest neuroscientific theories and unite several human learning capabilities such as episodic, emotional, procedural and causal learning.

Keywords

Cognitive agents Episodic learning Causal learning 

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References

  1. 1.
    Woolf, B.P.: Building Intelligent Interactive Tutors. Student Centered Strategies for revolutionizing e-learning. Morgan Kaufmann, Massachusetts (2009)Google Scholar
  2. 2.
    Faghihi, U., poirier, P., Fournier-Viger, P., Nkambou, R.: Human-Like Learning in a Conscious Agent. Journal of Experimental & Theoretical Artificial Intelligence (2010) (in Press)Google Scholar
  3. 3.
    Tulving, E.: Precis of Elements of Episodic Memory. Behavioural and Brain Sciences 7, 223–268 (1984)CrossRefGoogle Scholar
  4. 4.
    Purves, D., Brannon, E., Cabeza, R., Huettel, S.A., LaBar, K., Platt, M., Woldorff, M.: Principles of cognitive neuroscience. In: First, E. (ed.), Sunderland. Sinauer Associates, Massachusetts (2008)Google Scholar
  5. 5.
    Gopnik, A., Schulz, L. (eds.): Causal Learning: Psychology, Philosophy, and Computation. Oxford University Press, USA (2007)Google Scholar
  6. 6.
    Maldonado, A., Catena, A., Perales, J.C., Cándido, A.: Cognitive Biases in Human Causal Learning (2007)Google Scholar
  7. 7.
    Brauny, M., Rosenstiel, W., Schubert, K.-D.: Comparison of Bayesian Networks and Data Mining for Coverage Directed Verification. IEEE, Los Alamitos (2003)Google Scholar
  8. 8.
    Faghihi, U., Fournier-Viger, P., Nkambou, R., Poirier, P.: A Generic Causal Learning Model for Cognitive Agent. In: The Twenty Third International Conference on Industrial, Engineering & Other Applications of Applied Intelligent Systems, IEA-AIE 2010 (2010)Google Scholar
  9. 9.
    LeDoux, J.E.: Emotion circuits in the brain. Annu. Rev. Neurosci. 2000 23, 155–184 (2000)CrossRefGoogle Scholar
  10. 10.
    Morén, J.: Emotion and learning: a computational model of the amygdala. Lund University, Lund (2002)Google Scholar
  11. 11.
    Phelps, E.A.: Emotion and Cognition: Insights from studies of the human amygdala. Annual Review of Psychology 57, 27–53 (2006)CrossRefGoogle Scholar
  12. 12.
    Baars, B.J.: In the Theater of Consciousness: The Workspace of the Mind. Oxford University Press, Oxford (1997)CrossRefGoogle Scholar
  13. 13.
    Nkambou, R., Belghith, K., Kabanza, F.: An approach to intelligent training on a robotic simulator using an innovative path-planner. In: Ikeda, M., Ashley, K.D., Chan, T.-W. (eds.) ITS 2006. LNCS, vol. 4053, pp. 645–654. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  14. 14.
    Maes, P.: How to do the right thing. Connection Science 1, 291–323 (1989)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Thompson, R.F., Madigan, S.A.: Memory: The Key to Consciousness. Princeton University Press, Princeton (2007)Google Scholar
  16. 16.
    Alvarado, N., Adams, S., Burbeck, S.: The Role Of Emotion In An Architecture Of Mind. IBM Research (2002)Google Scholar
  17. 17.
    Damasio, A.R.: Looking for Spinoza: Joy, Sorrow and the Feeling Brain. Harcourt Inc., New York (2003)Google Scholar
  18. 18.
    Martin, C.B., Deutscher, M.: Remembering. Philosophical Review 75, 161–196 (1966)CrossRefGoogle Scholar
  19. 19.
    Shoemaker, S.: Persons and their Pasts. American Philosophical Quarterly 7, 269–285 (1970)Google Scholar
  20. 20.
    Perner, J.: Memory and Theory of Mind. In: Tulving, E., Craik, F.I.M. (eds.) The Oxford Handbook of Memory, pp. 297–312. Oxford University Press, Oxford (2000)Google Scholar
  21. 21.
    Bernecker, S.: The Metaphysics of Memory. Springer, Berlin (2008)CrossRefGoogle Scholar
  22. 22.
    Schoppek, W.: Stochastic Independence between Recognition and Completion of Spatial Patterns as a Function of Causal Interpretation. In: Proceedings of the 24th Annual Conference of the Cognitive Science Society (2002)Google Scholar
  23. 23.
    Schoppek, W.: Stochastic independence between recognition and completion of spatial patterns as a function of causal interpretation. In: Proceedings of the 24th Annual Conference of the Cognitive Science Society, pp. 804–809. Erlbaum, Mahwah (2002)Google Scholar
  24. 24.
    Anderson, J.R., Bothell, D., Byrne, M.D., Douglass, S., lebiere, C., Qin, Y.: An integrated theory of the mind. Psychological Review 111(4), 1036–1060 (2004)CrossRefGoogle Scholar
  25. 25.
    Gopnik, A., Glymour, C., Sobel, D.M., Schulz, L.E., Kushnir, T., Danks, D.: A Theory of Causal Learning in Children: Causal Maps and Bayes Nets. Psychological Review 111(1) (2004)Google Scholar
  26. 26.
    Braun, M., Rosenstiel, W., Schubert, K.-D.: Comparison of Bayesian networks and data mining for coverage directed verification category simulation-based verification. In: Eighth IEEE International, High-Level Design Validation and Test Workshop, 2003, pp. 91–95 (2003)Google Scholar
  27. 27.
    Hipp, J., Güntzer, U., Nakhaeizadeh, G.: Data Mining of Association Rules and the Process of Knowledge Discovery in Databases. In: Industrial Conference on Data Mining, pp. 15–36 (2002)Google Scholar
  28. 28.
    Deogun, J.S., Jiang, L.: Prediction Mining – An Approach to Mining Association Rules for Prediction. In: Ślęzak, D., Yao, J., Peters, J.F., Ziarko, W.P., Hu, X. (eds.) RSFDGrC 2005. LNCS (LNAI), vol. 3642, pp. 98–108. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  29. 29.
    Li, L., Deogun, J.S.: Discovering Partial Periodic Sequential Association Rules with Time Lag in Multiple Sequences for Prediction. In: Hacid, M.-S., Murray, N.V., Raś, Z.W., Tsumoto, S. (eds.) ISMIS 2005. LNCS (LNAI), vol. 3488, pp. 332–341. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  30. 30.
    Fournier-viger, P., Nkambou, R., Tseng, V.S.: RuleGrowth: Mining Sequential Rules Common to Several Sequences by Pattern-Growth. In: Proceedings of the 26th Symposium on Applied Computing (ACM SAC 2011), pp. 954–959 (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Usef Faghihi
    • 1
  • Philippe Fournier-Viger
    • 2
  • Roger Nkambou
    • 3
  1. 1.Dept. of Computer SciencesUniversity of MemphisUSA
  2. 2.Dept. of Computer SciencesNational Cheng-Kung UniversityTaiwan
  3. 3.Dept. of Computer SciencesUniversité du Québec à MontréalCanada

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