Advertisement

A Bayesian Approach to Attention Control and Concept Abstraction

  • Saied Haidarian Shahri
  • Majid Nili Ahmadabadi
Conference paper
  • 1.2k Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4840)

Abstract

Representing and modeling knowledge in the face of uncertainty has always been a challenge in artificial intelligence. Graphical models are an apt way of representing uncertainty, and hidden variables in this framework are a way of abstraction of the knowledge. It seems that hidden variables can represent concepts, which reveal the relation among the observed phenomena and capture their cause and effect relationship through structure learning. Our concern is mostly on concept learning of situated agents, which learn while living, and attend to important states to maximize their expected reward. Therefore, we present an algorithm for sequential learning of Bayesian networks with hidden variables. The proposed algorithm employs the recent advancements in learning hidden variable networks for the batch case, and utilizes a mixture of approaches that allows for sequential learning of parameters and structure of the network. The incremental nature of this algorithm facilitates gradual learning of an agent, through its lifetime, as data is gathered progressively. Furthermore inference is made possible, when facing a large corpus of data that cannot be handled as a whole.

Keywords

Bayesian Network Directed Acyclic Graph Attention Control Sequential Learning Hide Variable 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Chickering, D.M., Heckerman, D.: Efficient approximations for the marginal likelihood of Bayesian networks with hidden variables. Machine Learning 29, 181–212 (1997)CrossRefzbMATHGoogle Scholar
  2. 2.
    Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm. J. of the Royal Stat. Society B 39, 1–39 (1977)MathSciNetzbMATHGoogle Scholar
  3. 3.
    Drescher, G.L.: Made-up Minds. MIT Press, Cambridge (1991)zbMATHGoogle Scholar
  4. 4.
    Elidan, G., Friedman, N.: Learning Hidden Variable Networks: The Information Bottleneck Approach. JMLR 6, 81–127 (2005)MathSciNetzbMATHGoogle Scholar
  5. 5.
    Friedman, N., Goldszmidt, M.: Learning Bayesian networks with local structure. In: Proc. Twelfth Conf. on UAI, pp. 252–262. Morgan Kaufmann, San Francisco (1996)Google Scholar
  6. 6.
    Friedman, N., Goldszmidt, M.: Sequential Update of Bayesian Network Structure. In: Proc. Thirteenth Conf. on UAI, Rhode Island, pp. 165–174 (1997)Google Scholar
  7. 7.
    Friedman, N., Mosenzon, O., Slonim, N., Tishby, N.: Multivariate information bottleneck. In: Breese, J.S., Koller, D. (eds.) Proc. Seventeenth Conf. on UAI, San Francisco, pp. 152–161 (2001)Google Scholar
  8. 8.
    Friedman, N.: Learning belief networks in the presence of missing values and hidden variables. In: Fisher, D. (ed.) Proc. Fourteenth ICML, San Francisco, pp. 125–133 (1997)Google Scholar
  9. 9.
    Friedman, N.: The Bayesian structural EM algorithm. In: Proc. Fourteenth Conf. on UAI, San Francisco, pp. 129–138 (1998)Google Scholar
  10. 10.
    Heckerman, D., Geiger, D., Chickering, D.M.: Learning Bayesian networks: The combination of knowledge and statistical data. Machine Learning 20, 197–243 (1995)zbMATHGoogle Scholar
  11. 11.
    Jordan, M.I., Ghahramani, Z., Jaakkola, T., Saul, L.K.: An introduction to variational approximations methods for graphical models. In: Learning in Graphical Models, Kluwer, Dordrecht, Netherlands (1998)CrossRefGoogle Scholar
  12. 12.
    Lam, W., Bacchus, F.: Learning Bayesian belief networks: An approach based on the MDL principle. Computational Intelligence 10, 269–293 (1994)CrossRefGoogle Scholar
  13. 13.
    Neal, R.M., Hinton, G.E.: A new view of the EM algorithm that justifies incremental, Sparse, and other variants. In: Learning in Graphical Models, Kluwer, Dordrecht, Netherlands (1998)Google Scholar
  14. 14.
    Neal, R.M.: Probabilistic inference using Markov chain Monte Carlo methods. Technical Report CRG-TR-93-1, Dept. of Computer Science, University of Toronto (1993)Google Scholar
  15. 15.
    Piaget, J., Inhelder, B.: The Psychology of a Child. Basic Books, New York (1969)Google Scholar
  16. 16.
    Spirtes, P., Glymour, C., Scheines, R.: Causation, Prediction and Search. In: Number 81 in Lecture Notes in Statistics, Springer-Verlag, New York (1993)Google Scholar
  17. 17.
    Tishby, N., Pereira, F., Bialek, W.: The information bottleneck method. In: Proc. 37th Allerton Conference on Communication, Control and Computation, pp. 368–377. University of Illinois, US (1999)Google Scholar
  18. 18.
    Paletta, L., Rome, E., Buxton, H.: Attention Architectures for Machine Vision and Mobile Robots. In: Itti, L., Rees, G., Tsotsos, J. (eds.) Neurobiology of Attention, pp. 642–648. Academic Press, New York, NY (2005)CrossRefGoogle Scholar
  19. 19.
    Rizzolatti, G., Gentilucci, M.: Motor and visual-motor functions of the premotor cortex. In: Rakic, P., Singer, W. (eds.) Neurobiology of Neocortex, pp. 269–284. Wiley, Chichester (1988)Google Scholar
  20. 20.
    Haidarian, S., Rastegar, F., Nili, M.: Bayesian Approach to Learning Temporally Extended Concpets. In: CSICC 2007. Proceedings of the 12th International CSI Computer Conference, Tehran, Iran (2006)Google Scholar
  21. 21.
    Fatemi, H., Nili, M.: Biologically Inspired Framework for Learning and Abstract Representation of Attention Control. In: IJCAI 2007. Proceedings of the 4th International Workshop on Attention and Performance in Computational Vision at the International Joint Conference on Artificial Intelligence, Hyderabad, India (2007)Google Scholar
  22. 22.
    Horvitz, E., Kadie, C.M., Paek, T., Hovel, D.: Models of Attention in Computing and Communications: From Principles to Applications. Communications of the ACM 46(3), 52–59 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Saied Haidarian Shahri
    • 1
  • Majid Nili Ahmadabadi
    • 1
    • 2
  1. 1.Control and Intelligent Processing Center of Excelence, ECE Dept., University of TehranIran
  2. 2.School of Cognitive Sciences, Institute for studies in theoretical Physics and Mathematics, Niavaran, TehranIran

Personalised recommendations