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Probabilistic Learning

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

Abstract

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.

Keywords

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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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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