Eliciting configuration design heuristics with hidden Markov models
Editor: Anja Maier, Stanko Škec, Harrison Kim, Michael Kokkolaras, Josef Oehmen, Georges Fadel, Filippo Salustri, Mike Van der Loos
Author: McComb, Christopher; Cagan, Jonathan; Kotovsky, Kenneth
Institution: Carnegie Mellon University, United States of America
Section: Design Processes, Design Organisation and Management
Configuration design problems, characterized by the selection and assembly of components into a final desired solution, are common in engineering design. Although a variety of theoretical approaches to solving configuration design problems have been developed, little research has been conducted to observe how humans naturally attempt to solve such problems. This work mines the data from a cognitive study of configuration design to extract helpful design heuristics. The extraction of these heuristics is automated through the application of hidden Markov models. Results show that, for a truss configuration problem, designers proceed through four procedural states in solving configuration design problems, transitioning from topology design to shape and parameter design. High-performing designers are distinguished by their opportunistic tuning of parameters early in the process, enabling a heuristic search process similar to the A* search algorithm.