Adaptive generalisation

摘要

Adaptive generalisation is the ability to use prior knowledge in the performance of novel tasks. Thus, if we are to model intelligent behaviour with neural nets, they must be able to generalise across task domains. Our objective is to elucidate the aetiology of transfer of information between connectionist nets. First, a method is described that provides a standardised score for the quantification of how much task structure a net has extracted, and to what degree knowledge has been transferred between tasks. This method is then applied in three simulation studies to examine Input-to-Hidden (IH) and Hidden-to-Output (HO) decision hyperplanes as determinants of transfer effects. In the first study, positive transfer is demonstrated between functions that require the vertices of their spaces to be divided similarly, and negative transfer between functions that require decision regions of different shapes. In the other two studies, input and output similarity are varied independently in a series of paired associate learning tasks. Further explanation of transfer effects is provided through the use of a new technique that permits better visualisation of the entire computational space by showing both the relative position of inputs in Hidden Unit space, and the HO decision regions implemented by the set of weights.