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Solving Proportional Analogy Problems using Tensor Product Networks with Random Representations

This paper describes the use of vectors with randomly generated components for representing concepts in a system for solving proportional analogy problems using a memory based on a tensor product network. Both dense random vectors (all or most components non-zero) and sparse random vectors {most components zero) are used. In both cases systems which are able to solve proportional analogy problems were successfully produced: the degree of success vaned with the number of components in the vectors and/or the proportion of non-zero components.