Representation of Procedural Knowledge by a Model of Human Associative Processor (HASP)

A human associative processor (HASP) proposed by the author was developed and applied to procedural knowledge. The first step is a simple positive-feedback model in which the output of a conventional HASP is fed back to its own key input. The chain of a procedure is memorized by taking the positive-feedback input as the key input of the association, and by associating with the next procedure. A series of computer-simulation studies showed that the model could express control structures such as wait, jump, do-while, if-then, and case control procedures. The second step shows that the simple positive-feedback model sometimes cannot read the memorized procedure correctly; then an improved model was proposed. In the improved model structure, a converted orthogonal pattern from the positive feedback point and the external input key are used again as the key input. The improved model can read all the memorized procedures correctly; this was confirmed by computer simulation. It was also shown that the pattern completion function associated with the whole procedure from its part, can be represented by noise environmentalization.