Compositionality in a Knowledge-Based Constructive Learner

François Rivest and Thomas R. Shultz

A knowledge-based constructive learning algorithm, KBCC, simplifies and accelerates the learning of parity and chessboard problems. Previously learned knowledge of simpler versions of these problems is recruited in the service of learning more complex versions. A learned solution can be viewed as a composition in which the components are not altered, showing that concatenative compositionality can be achieved in neural terms.


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