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:
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reasoning about the surrounding world given observed data, thereby generating a representation - see Chapter 2 to recall what this means in terms of perception;
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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.
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Ferreira, J.F., Dias, J. (2014). Probabilistic Learning. In: Probabilistic Approaches to Robotic Perception. Springer Tracts in Advanced Robotics, vol 91. Springer, Cham. https://doi.org/10.1007/978-3-319-02006-8_6
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DOI: https://doi.org/10.1007/978-3-319-02006-8_6
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