An associative link from geometric to symbolic representations in artificial vision
Recent approaches to modelling the reference of internal symbolic representations of intelligent systems suggest to consider a computational level of a subsymbolic kind. In this paper the integration between symbolic and subsymbolic processing is approached in the framework of the research work currently carried on by the authors in the field of artificial vision. An associative mapping mechanism is defined in order to relate the constructs of the symbolic representation to a geometric model of the observed scene.
The implementation of the mapping mechanism by means of a neural network architecture is described taking into account both the backpropagation architecture and the Boltzmann machine architecture. Promising experimental results are discussed.
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