Implementation of emotional learning for cognitive tutoring agents

7th Mexican International Conference on Artificial Intelligence - Proceedings of the Special Session, MICAI 2008

Volumn , Issue , 2008, Pages 310-316

  Faghihi, Usef ^a   Poirier, Pierre ^a   Dubois, Daniel ^a   Gaha, Mohamed ^a   Nkambou, Roger ^a  

^a UNIVERSITÉ DU QUÉBEC À MONTRÉAL   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

CHANGING ENVIRONMENTS; EMOTIONAL LEARNINGS; LEARNING MECHANISMS; PROPOSED ARCHITECTURES;

AGENTS; ARTIFICIAL INTELLIGENCE;

EDUCATION;

EID: 57849145753     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/MICAI.2008.45     Document Type: Conference Paper

References (21)

1. Baars, B. J., and Franklin, S. (2003). How conscious experience and working memory interact. Trends in Cognitive Science 7:166-172.
2. Cannon, W. (1927). The James-Lange theory of emotion: a critical examination and an alternative theory. Am. J. Psychol.39, pp. 106-124.
3. Critchley, H., & al, e. (2002). Fear conditioning in humans: the influence of awareness and autonomic arousal on functional neuroanatomy. Neuron 33, pp. 653-663.
4. Dubois, D. R. (2006). "How 'Consciousness' Allows a Cognitive Tutoring Agent Make Good Diagnosis During Astronauts' Training". Proceedings of the 8th International Conference on Intelligent Tutoring Systems (ITS'2006) LNCS no 4053.,
5. Faghihi, U., & Dubois, D. N. (2007). Learning Mechanisms for a Tutoring Cognitive Agent. Proceedings of the 7th IEEE International Conference on Advanced Learning Technologies (ICALT 2007), July 18-20, Niigata, Japan.
6. Franklin, S., & Patterson, F. (2006). The L1DA architecture: Adding new modes of learning to an intelligent, autonomous, software agent. Society for Design and Process Science, Integrated Design and Process Technology, IDPT-2006.
7. Franklin, S., & Ramamurthy, U. (2006). Motivations, Values and Emotions: Three Sides of the same Coin. Proceedings of the Sixth International Workshop on Epigenetic Robotics, Paris, France, September 2006.,
8. Gratch, J., & Marsella, S. (2004). A Domain-independent Framework for Modeling Emotion.. Journal of Cognitive Systems Research, 5, 4, 269-306.
9. Hirt, E., McDonald, H., & Melton, R. (1996). Processing goals and the affect-performance link: mood as main effect or mood as an input? In: L.L. Martin and A. Tesser, Editors, Striving and feeling: Interactions among goals,affect, and self-regulation, Erlbaum, Mahwah, NJ (1996), pp. 303-328.
10. Hofstadter, D. R., and Mitchell, M. (1994). The Copycat Project: A model of mental fluidity and analogy-making. In Advances in connectionist and neural com putation theory, Vol. 2: logical connections, ed. K. J. Holyoak, and J. A. Barnden. Norwood N.J.: Ablex.
11. Jackson, J. V. (1987). Idea for a Mind. Siggart Newsletter, 181:23-26.
12. James, W. (1884). What is an emotion? Mind 9, pp. 188-205.
13. Ledoux, J. E. (2000). Emotion circuits in the brain.. Annual Review of Neuroscience, 23, 155-184..
14. Maes, P. (1989). How to do the right thing. Connection Science 1:291-323.
15. Ortony, A., Clore, G. L., & and Collins, A. (1988). The cognitive structure of emotions. Cambridge, UK, Cambridge University Press.
16. Phelps, E. (2006). Emotion and Cognition: Insights from studies of the human amygdala. Annual Review of Psychology, 57, 27-53.
17. Rolls, E. T. (2000). Neurophysiology and functions of the primate amygdala, and the neural basis of emotion. In The Amygdala: a Functional Analysis (ed. Aggleton, J. P.).. Oxford Univ. Press, Oxford, UK, p.447-478..
18. Squire, R, L., and Kandel, R, Eric. (1998). Memory from mind to molecules, page 167-171.
19. Tyrrell, T. (1994). An evaluation of Maes' bottom-up mechanism for behavior selection.. Adaptive Behavior 2(4):307-348.
20. Velásquez, J. (1996). Cathexis: A Computational model for the Generation of Emotions and Their Influence in the. Behavior of Autonomous Agents. S.M. Thesis Department of Electrical Engineering and Computer Science.Massachusetts Institute of Technology.
21. Morén, J. (2002). Emotional and learning :A Computational Model of the Amygdala.