Probabilistic Learning

  • João Filipe Ferreira
  • Jorge Dias
Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 91)


An intuitive tell-tale of intelligence is the ability animals possess, particularly humans, of learning from experience. So, in fact, when we set out in designing truly intelligent systems in robotics, the general aim is to conjure up an architecture that is equally capable of:

  • reasoning about the surrounding world given observed data, thereby generating a representation - see Chapter 2 to recall what this means in terms of perception;

  • learning better representations for the future from the data it is gathering in the present, therefore preparing for generalisation - i.e., increasing cognitive performance by refining its internal model of the world as new data becomes available.


Bayesian Network Multinomial Distribution Structure Learning Sensor Model Parameter Learn 
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.


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  1. 1.
    Tenenbaum, J.B., Kemp, C., Griffiths, T.L., Goodman, N.D.: How to grow a mind: Statistics, structure, and abstraction. Science 331(6022), 1279–1285 (2011)MathSciNetzbMATHCrossRefGoogle Scholar
  2. 2.
    Colas, F., Diard, J., Bessiére, P.: Common Bayesian Models For Common Cognitive Issues. Acta Biotheoretica 58(2-3), 191–216 (2010)CrossRefGoogle Scholar
  3. 3.
    Darwiche, A.: Modeling and reasoning with Bayesian networks. Cambridge University Press, Cambridge (2009)zbMATHCrossRefGoogle Scholar
  4. 4.
    Ferreira, J.F., Pinho, C., Dias, J.: Implementation and Calibration of a Bayesian Binaural System for 3D Localisation. In: 2008 IEEE International Conference on Robotics and Biomimetics (ROBIO 2008), Bangkok, Thailand (2009)Google Scholar
  5. 5.
    Fox, E.: Bayesian Nonparametric Learning of Complex Dynamical Phenomena. Ph.D. thesis, MIT, Cambridge, MA (2009)Google Scholar
  6. 6.
    Koller, D., Friedman, N.: Probabilistic graphical models: principles and techniques. MIT Press (2009)Google Scholar
  7. 7.
    Hy, R.L., Bessiére, P.: Probabilistic Reasoning and Decision Making in Sensory-Motor Systems. In: Bessiére, P., Laugier, C., Siegwart, R. (eds.) Playing to Train Your Video Game Avatar. STAR, vol. 46, pp. 263–278. Springer, Heidelberg (2008)Google Scholar
  8. 8.
    Kemp, C., Tenenbaum, J.B.: The discovery of structural form. Proceedings of the National Academy of Sciences 105(31), 10687–10692, 1091–6490 (2008), doi:10.1073/pnas.0802631105 ISSN 0027-8424, PMID: 18669663Google Scholar
  9. 9.
    Bergemann, D., Välimäki, J.: Bandit problems. Technical report, Cowles Foundation for Research in Economics, Yale University (2006)Google Scholar
  10. 10.
    Florez-Larrahondo, G.: Incremental learning of discrete hidden markov models. Ph.D. thesis, Mississippi State University, Mississippi State, MS, USA. AAI3193417 (2005)Google Scholar
  11. 11.
    Kaelbling, L.P., Littman, M.L., Moore, A.W.: Reinforcement Learning: A Survey. Journal of Artificial Intelligence Research 4, 237–285 (1996)Google Scholar
  12. 12.
    Buntine, W.L.: Operations for Learning with Graphical Models. Journal of Artificial Intelligence Research (AI Access Foundation) 2, 159–225 (1994) ISSN 11076-9757Google Scholar
  13. 13.
    Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum Likelihood from Incomplete Data via the EM Algorithm. Journal of the Royal Statistical Society Series B (Methodological) 39(1), 1–38 (1977)MathSciNetzbMATHGoogle Scholar
  14. 14.
    Baum, L.E., Petrie, T., Soules, G., Weiss, N.: A maximization technique occurring in the statistical analysis of probabilistic functions of Markov chains. The Annals of Mathematical Statistics 41(1), 164–171 (1970)MathSciNetzbMATHCrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Instituto de Sistemas e Robotica, Departamento de Engenharia Electrotécnica e Computadores Pinhal de Marrocos, Pólo II Universidade de CoimbraCoimbraPortugal

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