Advertisement

Self-organizing Cognitive Models for Virtual Agents

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8108)

Abstract

Three key requirements of realistic characters or agents in virtual world can be identified as autonomy, interactivity, and personification. Working towards these challenges, this paper proposes a brain inspired agent architecture that integrates goal-directed autonomy, natural language interaction and human-like personification. Based on self-organizing neural models, the agent architecture maintains explicit mental representation of desires, intention, personalities, self-awareness, situation awareness and user awareness. Autonomous behaviors are generated via evaluating the current situation with active goals and learning the most appropriate social or goal-directed rule from the available knowledge, in accordance with the personality of each individual agent. We have built and deployed realistic agents in an interactive 3D virtual environment. Through an empirical user study, the results show that the agents are able to exhibit realistic human-like behavior, in terms of actions and interaction with the users, and are able to improve user experience in virtual environment.

Keywords

Cognitive models Virtual agents Self-Organizing neural networks Autonomy Personality Interactivity 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Anderson, J.R., Bothell, D., Byrne, M.D., Douglass, S., Lebiere, C., Qin, Y.: An integrated theory of the mind. Psychological Review 111, 1036–1060 (2004)CrossRefGoogle Scholar
  2. 2.
    Animesh, A., Pinsonneault, A., Yang, S.-B., Oh, W.: An odyssey into virtual worlds: Exploring the impacts of technological and spatial environments. MIS Quaterly 35, 789–810 (2011)Google Scholar
  3. 3.
    Bogacz, R., Gurney, K.: The basal ganglia and cortex implement optimal decision making between alternative actions. Neural Computation 19(2), 442–477 (2007)MathSciNetzbMATHCrossRefGoogle Scholar
  4. 4.
    Carpenter, G.A., Grossberg, S.: Adaptive Resonance Theory. In: The Handbook of Brain Theory and Neural Networks, pp. 87–90. MIT Press (2003)Google Scholar
  5. 5.
    Gerhard, M., Moore, D.J., Hobbs, D.J.: Embodiment and copresence in collaborative interfaces. Int. J. Hum.-Comput. Stud. 64(4), 453–480 (2004)CrossRefGoogle Scholar
  6. 6.
    Haykin, S.: Neural Network: A Comprehensive Foundation. Prentice Hall (1999)Google Scholar
  7. 7.
    Jan, D., Roque, A., Leuski, A., Morie, J., Traum, D.: A virtual tour guide for virtual worlds. In: Ruttkay, Z., Kipp, M., Nijholt, A., Vilhjálmsson, H.H. (eds.) IVA 2009. LNCS, vol. 5773, pp. 372–378. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  8. 8.
    Kasap, Z., Thalmann, N.: Intelligent virtual humans with autonomy and personality: State-of-the-art. Intelligent Decision Technologies 1, 3–15 (2007)Google Scholar
  9. 9.
    Kelley, T.D.: Symbolic and sub-symbolic representations in computational models of human cognition: What can be learned from biology? Theory and Psychology 13(6), 847–860 (2003)CrossRefGoogle Scholar
  10. 10.
    Kopp, S., Gesellensetter, L., Krämer, N.C., Wachsmuth, I.: A conversational agent as museum guide – design and evaluation of a real-world application. In: Panayiotopoulos, T., Gratch, J., Aylett, R.S., Ballin, D., Olivier, P., Rist, T. (eds.) IVA 2005. LNCS (LNAI), vol. 3661, pp. 329–343. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  11. 11.
    Laird, J.E., Newell, A., Rosenbloom, P.S.: Soar: An architecture for general intelligence. Artificial Intelligence 33, 1–64 (1987)CrossRefGoogle Scholar
  12. 12.
    Langley, P., Choi, D.: A unified cognitive architecture for physical agents. In: Proceedings of 21st National Conference on Artificial Intelligence, pp. 1469–1474 (2006)Google Scholar
  13. 13.
    Lebiere, C., Wallach, D.: Sequence learning in the act-r cognitive architecture: Empirical analysis of a hybrid model. In: Sun, R., Giles, C.L. (eds.) Sequence Learning. LNCS (LNAI), vol. 1828, pp. 188–212. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  14. 14.
    Mccrae, R., Costa, P.: An introduction to the five-factor model and its applications. Journal of Personality 60, 172–215 (1992)Google Scholar
  15. 15.
    Nah, F., Eschenbrenner, B., DeWester, D.: Enhancing brand equity through flow and telepresence: A comparison of 2d and 3d virtual worlds. MIS Quaterly 35, 731–748 (2011)Google Scholar
  16. 16.
    O’Reilly, R.C., Frank, M.J.: Making working memory work: A computational model of learning in the prefrontal cortex and basal ganglia. Neural Computation 18, 283–328 (2006)MathSciNetzbMATHCrossRefGoogle Scholar
  17. 17.
    Prescott, T.J., Gonzalez, F.M.M., Gurney, K., Humphries, M.D., Redgrave, P.: A robot model of the basal ganglia: Behavior and intrinsic processing. Neural Networks 19(1), 31–61 (2006)zbMATHCrossRefGoogle Scholar
  18. 18.
    Rousseau, D., Roth, B.: A social-psychological model for synthetic actors. In: Proceedings of 2nd International Conference on Autonomous Agents, pp. 165–172 (1997)Google Scholar
  19. 19.
    Schultz, W.: Getting formal with dopamine and reward. Neuron 36(2), 241–263 (2002)CrossRefGoogle Scholar
  20. 20.
    Sun, R., Merrill, E., Peterson, T.: From implicit skills to explicit knowledge: a bottom-up model of skill learning. Cognitive Science 25(2), 203–244 (2001)CrossRefGoogle Scholar
  21. 21.
    Tan, A.-H.: FALCON: A fusion architecture for learning, cognition, and navigation. In: Proceedings of International Joint Conference on Neural Networks, pp. 3297–3302 (2004)Google Scholar
  22. 22.
    Tan, A.-H.: Direct Code Access in Self-Organizing Neural Networks for Reinforcement Learning. In: Proceedings of International Joint Conference on Artificial Intelligence, pp. 1071–1076 (2007)Google Scholar
  23. 23.
    Tan, A.-H., Carpenter, G.A., Grossberg, S.: Intelligence through interaction: Towards a unified theory for learning. In: Liu, D., Fei, S., Hou, Z.-G., Zhang, H., Sun, C. (eds.) ISNN 2007, Part I. LNCS, vol. 4491, pp. 1094–1103. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  24. 24.
    Tan, A.-H., Lu, N., Xiao, D.: Integrating Temporal Difference Methods and Self-Organizing Neural Networks for Reinforcement Learning with Delayed Evaluative Feedback. IEEE Transactions on Neural Networks 9(2), 230–244 (2008)Google Scholar
  25. 25.
    Wallace, R.S.: The anatomy of A.L.I.C.E. Tech. report, ALICE AI Foundation (2000)Google Scholar
  26. 26.
    Weizenbaum, J.: ELIZA: a computer program for the study of natural language communication between men and machines. Communications of the ACM 9 (1996)Google Scholar
  27. 27.
    Yi, M.Y., Hwang, Y.: Predicting the use of web-based information systems: self-efficacy, enjoyment, learning goal orientation, and the technology acceptance model. International Journal of Computer-Human Studies 59, 431–449 (2003)CrossRefGoogle Scholar
  28. 28.
    Yoon, S., Burke, R.C., Blumberg, B.M., Schneider, G.E.: Interactive training for synthetic characters. In: AAAI, pp. 249–254 (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.School of Computer EngineeringNanyang Technological UniversitySingaporeSingapore

Personalised recommendations