The Learning Tree, A New Concept in Learning

• In this paper learning is considered to be the bootstrapping procedure where fragmented past experience of what to do when performing well is used for generation of new responses adding more information to the system about the environment. The gained knowledge is represented by a behavior probability density function which is decomposed into a number of normal distributions using a binary tree. This tree structure is built by storing highly reinforced stimuli-response combinations, decisions, and calculating their mean decision vector and covariance matrix. Thereafter the decision space is divided, through the mean vector, into two halves along the direction of maximal data variation. The mean vector and the covariance matrix are stored in the tree node and the procedure is repeated recursively for each of the two halves of the decision space forming a binary tree with mean vectors and covariance matrices in its nodes. The tree is the systems guide to response generation. Given a stimuli the system searches for decisions likely to give a high reinforcement. This is accomplished by treating the sum of the normal distributions in the leaves, using their mean vectors and covariance matrices as the distribution parameters, as a distribution describing the systems behavior. A response is generated by fixating the stimuli in this sum of normal distribution and use the resulting distribution, which turns out to be a new sum of normal distributions, for random generation of the response. This procedure will also make it possible for the system to have several equally plausible response to one stimuli when this is appropriate. Not applying maximum likelihood principles will lead to a more explorative system behavior avoiding local minima traps.

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