JRM Vol.17 No.6 pp. 689-696
doi: 10.20965/jrm.2005.p0689


Probabilistic Human Modeling Based on Personal Construct Theory

Yoichi Motomura, and Takeo Kanade

Digital Human Research Center, The National Institute of Advanced Industrial Science and Technology / CREST, JST

January 28, 2005
April 1, 2005
December 20, 2005
personal construct theory, cognitive human model, Bayesian network, evaluation grid, probabilistic modeling

We have initiated a project for constructing a mathematical model of human cognitive and psychological functions, executable on a computer. To this end, we propose probabilistic modeling based on the Personal Construct Theory, a basic theory used in cognitive/evaluative structure models for individuals. After extracting a skeleton structure using the Evaluation Grid, Bayesian network model is constructed though data learning. By executing a probabilistic reasoning algorithm on the constructed model, our proposal is applied to user-adaptable information systems, information recommendation, car navigation systems, etc.

Cite this article as:
Yoichi Motomura and Takeo Kanade, “Probabilistic Human Modeling Based on Personal Construct Theory,” J. Robot. Mechatron., Vol.17, No.6, pp. 689-696, 2005.
Data files:
  1. [1] T. Kanade, and M. Mochimaru, “Digital Human,” Journal of ISCIE, Systems, Control and Information, Vol.46, pp. 453-458, 2002.
  2. [2] G. A. Kelly, “The Psychology of Personal Constructs,” Norton, New York, 1955.
  3. [3] J. Sanui, and G. Maruyama, “Revealing of Preference Structure by the Evaluation Grid Method,” Proceedings of the Seventh International Conference on Human-Computer Interaction, pp. 471-474, 1997.
  4. [4] J. Gutman, “A means-end chain model based on consumer categorization process,” Journal of Marketing, 46, pp. 60-72, 1982.
  5. [5] M. Mayanagi, “Buying structure analysis of Cup type Icecream,” proc. of sensory evaluation symposium, pp. 105-112, NIKKAGIREN, 1999.
  6. [6] M. Mayanagi, “Investigation about evaluation structure in buying Milk,” proc. of sensory evaluation symposium, pp. 41-46, NIKKAGIREN, 2000.
  7. [7] M. Haga, “3-step research,” proc. of the sixth Annual meeting of Japan Society of KANSEI engineering, 2004.
  8. [8] M. Haga et al., “Evaluation Grid Method,” Proc. of the eighty first symposium on Behaviormetric, 2004.
  9. [9] T. Murakami, A. Suyama, and R. Orihara, “Consumer Behavior Analysis using Bayesian Networks,” Technical Report of IEICE, NC2004-70, pp. 9-14, 2004.
  10. [10] Y. Motomura, “BAYONET: Bayesian network on neural network,” Foundation of Real World Intelligence, pp. 28-37, CSLI lecture note 125, CSLI publications Calfornia, 2001.
  11. [11] Y. Motomura, “Probabilistic reasoning algorithms and their experiments in Bayesian networks,” Technical Report of IEICE, NC2003-220, pp. 157-162, 2004.
  12. [12] H. Iwasaki, N. Mizuno, K. Hara, and Y. Motomura, “Applying Bayesian Network to Car Navigation systems that recommend contents corresponding to users’ preference,” Technical Report of IEICE, NC2004-55, pp. 25-30, 2004.
  13. [13] C. Ono, Y. Motomura, and H. Asoh, “Study of movie recommendation by Bayesian networks,” Technical Report of IEICE, NC2004-66, pp. 25-30, 2004.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Mar. 05, 2021