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A connectionist model of the acquisition of common sensereasoningMikael

The overall aim of the paper is to demonstrate that, from a machine learning point of view, connectionist networks are not black boxes. Trained networks contain rich and varied internal representations gleaned from training sets. Analysis of these representations can provide useful results concerning the generalizability of these networks to novel examples. More particularly in the case of common sense reasoning, the recently developed technique of hyperplane analysis 1 can demonstrate the adaptive power of connectionist networks when presented with information concerning new entities and the ability of such networks to cope with exceptions to what has already been input in the training set. One important advantage of such analysis is that machine learning researchers need not be committed to any particular form of symbolic structure but can study the process of acquiring common sense reasoning as well as the impact of new information on existing internal representations purely from the viewpoint of learning. Another advantage is that the analytical methods are formally grounded in a precise and rigorous mathematical framework which ultimately may have more explanatory power than that provided by classical symbolic accounts alone.