Mining Process Heuristics From Designer Action Data via Hidden Markov Models

Configuration design problems, characterized by the assembly of components into a final desired solution, are common in engineering design. Various theoretical approaches have been offered for solving configuration type problems, but few studies have examined the approach that humans naturally use to solve such problems. This work applies data-mining techniques to quantitatively study the processes that designers use to solve configuration design problems. The guiding goal is to extract beneficial design process heuristics that are generalizable to the entire class of problems. The extraction of these human problem-solving heuristics is automated through the application of hidden Markov models to the data from two behavioral studies. Results show that designers proceed through four procedural states in solving configuration design problems, roughly 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 more effective and nuanced search for solutions. [DOI: 10.1115/1.4037308]

[1]  Raymonde Guindon.  Designing the design process: exploiting opportunistic thoughts , 1990 .

[2]  Sylvain Arlot,et al.  A survey of cross-validation procedures for model selection , 2009, 0907.4728.

[3]  Christopher McComb,et al.  Capturing Human Sequence-Learning Abilities in Configuration Design Tasks through Markov Chains , 2017 .

[4]  Christopher McComb,et al.  Validating a Tool for Predicting Problem-Specific Optimized Team Characteristics , 2017 .

[5]  B Bender,et al.  ON THE SUPERIORITY OF OPPORTUNISTIC DESIGN STRATEGIES DURING EARLY EMBODIMENT DESIGN , 2004 .

[6]  Christopher McComb,et al.  Lifting the Veil: Drawing insights about design teams from a cognitively-inspired computational model , 2015 .

[7]  D. Haussler,et al.  Hidden Markov models in computational biology. Applications to protein modeling. , 1993, Journal of molecular biology.

[8]  A.M. Gonzalez,et al.  Modeling and forecasting electricity prices with input/output hidden Markov models , 2005, IEEE Transactions on Power Systems.

[9]  John R. Anderson,et al.  Discovering the Sequential Structure of Thought , 2014, Cogn. Sci..

[10]  Durbin,et al.  Biological Sequence Analysis , 1998 .

[11]  Bob J. Wielinga,et al.  Configuration-Design Problem Solving , 1997, IEEE Expert.

[12]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[13]  Christopher McComb,et al.  Optimizing Design Teams Based on Problem Properties: Computational Team Simulations and an Applied Empirical Test , 2017 .

[14]  Ladislau Bölöni,et al.  Analyzing Team Actions with Cascading HMM , 2009, FLAIRS.

[15]  Sanjay Mittal,et al.  Towards a Generic Model of Configuraton Tasks , 1989, IJCAI.

[16]  Mi Jeong Kim,et al.  The impact of tangible user interfaces on spatial cognition during collaborative design , 2008 .

[17]  Cynthia J. Atman,et al.  Engineering Design Processes: A Comparison of Students and Expert Practitioners , 2007 .

[18]  Elies Dekoninck,et al.  The appearance of creative behavior in later stage design processes , 2014 .

[19]  Willemien Visser,et al.  Organisation of Design Activities: Opportunistic, with Hierarchical Episodes , 1994, Interact. Comput..

[20]  Paul Rodgers,et al.  The design thinking approaches of three different groups of designers based on self-reports , 2013 .

[21]  H. Simon,et al.  Why are some problems hard? Evidence from Tower of Hanoi , 1985, Cognitive Psychology.

[22]  L. Baum,et al.  An inequality with applications to statistical estimation for probabilistic functions of Markov processes and to a model for ecology , 1967 .

[23]  Petra Badke-Schaub,et al.  Thinking in design teams - an analysis of team communication , 2002 .

[24]  Lalit R. Bahl,et al.  Design of a linguistic statistical decoder for the recognition of continuous speech , 1975, IEEE Trans. Inf. Theory.

[25]  Christopher McComb,et al.  Rolling with the punches: An examination of team performance in a design task subject to drastic changes , 2015 .

[26]  L. Baum,et al.  Statistical Inference for Probabilistic Functions of Finite State Markov Chains , 1966 .

[27]  L. Baum,et al.  A Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov Chains , 1970 .

[28]  R. L. Stratonovich.  CONDITIONAL MARKOV PROCESSES , 1960 .

[29]  Christopher McComb,et al.  Eliciting configuration design heuristics with hidden Markov models , 2017 .

[30]  John R. Anderson,et al.  Discovering Processing Stages by combining EEG with Hidden Markov Models , 2013, CogSci.

[31]  Caitlin Tenison,et al.  Modeling the distinct phases of skill acquisition. , 2016, Journal of experimental psychology. Learning, memory, and cognition.

[32]  B. Chandrasekaran,et al.  Design Problem Solving: A Task Analysis , 1990, AI Mag..

[33]  L. Baum,et al.  Growth transformations for functions on manifolds. , 1968 .