Learning of associative prediction by experience

We introduce a neuronal model which learns during problem solving the associative prediction by experience. The model uses picture representation rather than symbolic representation to perform problem solving. Consequently the computational task corresponds to the manipulation of pictures. A computation is performed with the aid of associations by the transformation from an initial state represented as a picture to a desired state represented as a picture. Picture representation enables learning from examples through the definition of similarity between different problems. The solved problems are reused to speed up the search for related or similar problems. The model learns by experience of failures and successes by an associative memory in which pictorial sequences of states describing plans are stored. The learning with different strategies is demonstrated by empirical experiments in the block world. It is shown that depending on the learning strategy learning improves the behavior of the model in significant manner. (C) 2002 Elsevier Science B.V. All rights reserved.