The Effects of Language and Pruning on Function Structure Interpretability

Abstract

<jats:p>In this research, the interpretability of function structures is evaluated through a user study in which participants are given function structures and asked to identify the product that is modeled. Two abstraction factors are controlled in the experiment: the type of functions and the specificity of the terms, thus resulting in functional models are four level of abstraction. The user study shows that free language significantly improves the accuracy and speed of human interpretability over the functional basis vocabulary. Further, pruned function structures significantly improve the speed of interpretability over reverse-engineered function structures without a loss of accuracy. It is concluded that the levels of each factor are useful for different activities and stages of design. Recommendations are made for the appropriate combinations of factor levels for various design activities.</jats:p>

Empirical Studies of Functional Decomposition in Early Design

“…We provided a list of function verbs from the pruned functional basis [30], but did not enforce using these words. Teams worked on their functional decomposition diagram during lab time with help from the instructors, but with no help from the researchers.…”

“…Research papers on functional decomposition in early design propose or discuss functional decomposition methods [18,[23][24][25][26][27], develop taxonomies [28][29][30], propose educational interventions [31], or debate theory [32,33], such as definitions [4,34]. We omit from our search any papers which consider functions for mathematical modeling or methods that consider pre-existing artifacts.…”

Function in engineering: Benchmarking representations and models

“…In such a case, the familiarity of the modeling approach and the intuitiveness of the representation then becomes a major issue when inferring information. Several studies have been undertaken to study the authorship and consistency and interpretability of models (Kurfman et al, 2003; Caldwell, Ramachandran, et al, 2012; Caldwell, Thomas, et al, 2012), as well as elucidating the correctness of model construction (Nagel et al, 2015). Alternatively, some approaches have been proposed that automatically reason on function models from database collections (Lucero et al, 2014; Patel, Andrews, et al, 2016; Sridhar et al, 2016).…”

Comparing function structures and pruned function structures for market price prediction: An approach to benchmarking representation inferencing value

“…Previous research has focused on quantifying the information content of function models in an information theoretic approach (Sen, Caldwell, et al, 2010; Sen, Summers, et al, 2010) and also by means of user studies (Caldwell et al, 2010; Caldwell, Ramachandran, et al, 2012; Caldwell, Thomas, et al, 2012). An information metric was developed that is applicable to function structures constructed using vocabulary and nouns from a predetermined set of the same (Sen, Caldwell, et al, 2010).…”

“…Moreover, the functional modeling representation helps engineers in ideation by providing means to abstract and decompose problems so as to manipulate partial solutions (Otto & Wood, 2001). Previous research has helped to develop a deeper understanding of how much value this design representation has in terms of information content and designer interpretability (Caldwell & Mocko, 2008; Sen, Caldwell, et al, 2010; Caldwell, Thomas, et al, 2012). The objective of the current research is to develop a deeper understanding of the value contained in this representation from a computational or information-centric perspective.…”