Analogical Matching Using Device-Centric and Environment-Centric Representations of Function

Design is hard and needs to be supported by software. One of the ways software can support designers is by providing analogical reasoning. To make analogical reasoning work well, the software makers need to know how to create a knowledge representation that will facilitate the kind of analogies that the designers want. This thesis will inform software makers by experimenting with two kinds of knowledge representations, called device-centric (DC) and environment-centric (EC), and to try to determine the relative benefits of using either one of them for analogical matching. We performed computational experiments, using Structure Mapping Engine for matching, to determine the quantity and quality of analogical matches that are produced when the representation is varied. We conducted a limited human experiment, using questionnaires and repertory grids, to determine if any of the computational results were novel, and to determine if the human similarity ratings between devices correlated with the computer results. We show that design software should use DC representations to produce a few focused matches which have high average weight. It should use EC representations to produce many matches some of high weight and some of low weight. Based on our human experiment, design software can use either DC or EC representations to produce novel matches. Our experiments also show that human matches correlate most strongly with a combined DC and EC representation and that their similarity reasons are more EC than DC. This suggests that designers tend to think more in EC terms than in DC terms.