Compound Analogical Design, Or How to Make a Surfboard Disappear Michael E. Helms, Swaroop Vattam & Ashok Goel Design Intelligence Laboratory, School of Interactive Computing Georgia Institute of Technology, Atlanta, Georgia, USA {mhelms3, svattam, goel}@cc.gatech.edu Abstract Biologically-inspired design uses analogous biological systems to develop novel solutions for human needs. In this paper we describe an in situ cognitive study of biologically inspired engineering design. We found that biologically inspired engineering design often involves compound analogies in which a new design concept is generated by composing the results of multiple cross-domain analogies. This process of compound analogy relies on an opportunistic interaction between two processes: problem decomposition and analogical transfer. Based on this cognitive study, we also present an information- processing account of compound analogies. Keywords: design, cognitive study, biologically inspired, engineering, analogical design. 1. Introduction One of the conundrums in research on creativity is that any solution to any problem has to start from what one already knows: so, how is it possible to create novel solutions? One way of trying to answer this question is to conduct in situ (or in vivo) cognitive studies of creativity in naturalistic settings (e.g., Darden & Cook 1994; Dunbar 1995; Kurz-Milcke, Nersessian & Newstetter 2004; Christensen & Schunn 2008). Kurz- Milcke, Nersessian & Newstetter (2004), for example, describe an in situ cognitive study of scientific research laboratories in bio-medical engineering. We have conducted an in situ cognitive study of biologically inspired design, and in this paper we describe one of the (many) findings from the study. Biologically inspired design is a growing movement in design, driven in part by the need for environmentally sustainable development (e.g., Benyus 1997). Although applications of biologically inspired design can be found in many design domains such as engineering, architecture and computing, our focus is on biologically inspired engineering design (and in the rest of this paper we will use the term biologically inspired design to refer to biologically inspired engineering design only). A recent example from textile engineering is the design of thermally self- regulating clothing based on the design of pinecones (Vincent & Mann 2002). By definition, biologically inspired design is based on cross-domain analogies. We chose this particular domain for study because of our interest in understanding the role, process and content of analogies in creativity. 2. Cognitive Study Our study was conducted in the context of an interdisciplinary course on biologically inspired design offered by Georgia Tech’s Center for Biologically Inspired Design in the fall of 2006. At least 32 of the 45 students in the class had already taken a course in design and/or participated in design projects as part of their undergraduate education. In the rest of this paper, we will refer to the students in the class as designers. The instructors of the interdisciplinary course taught biologically inspired design using a problem-based learning approach. Design projects grouped an interdisciplinary team of 4-5 students together, where each team had one student from biology and the rest were from different engineering disciplines. Each team was responsible for identifying a problem that could be addressed by a biologically inspired solution, exploring solution alternatives, and developing a final solution design based on biological design solutions. As observers, we attended the classroom sessions, collected course materials, documented lecture content, and observed teacher-student and student-student interactions in the classroom. We had no influence on the course design or pedagogical approach. We also did in situ observations of a few of the teams engaged in their design projects. We minimized our intervention, only occasionally asking clarifying questions. Our observations focused on the cognitive practices and products of the designers. We observed and documented the frequently occurring problem-solving and representational activities of designers as part of the design process. Some of these activities were part of the design process explicitly taught by the instructors. Others emerged during practice. In terms of the design products, we observed and documented the natural evolution of the conceptual design over time. We also attended the final oral presentations of the design teams to the class, and read the design briefs the teams submitted with their projects. Vattam, Helms & Goel (2007) provide details of the cognitive study.
[1]
Ann Q. Gates,et al.
TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
,
2005
.
[2]
Bo T. Christensen,et al.
The relationship of analogical distance to analogical function and preinventive structure: the case of engineering design
,
2007,
Memory & cognition.
[3]
Nancy J. Nersessian,et al.
What Has History to Do with Cognition? Interactive Methods for Studying Research Laboratories ∗
,
2004
.
[4]
L. H. Shu,et al.
Using language as related stimuli for concept generation
,
2007,
Artificial Intelligence for Engineering Design, Analysis and Manufacturing.
[5]
Ashok K. Goel,et al.
Design, innovation and case-based reasoning
,
2005,
The Knowledge Engineering Review.
[6]
Padraig Cunningham,et al.
Hierarchical Case-Based Reasoning Integrating Case-Based and Decompositional Problem-Solving Techniques for Plant-Control Software Design
,
2001,
IEEE Trans. Knowl. Data Eng..
[7]
J. Vincent,et al.
Systematic technology transfer from biology to engineering
,
2002,
Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.
[8]
Amaresh Chakrabarti,et al.
A functional representation for aiding biomimetic and artificial inspiration of new ideas
,
2005,
Artificial Intelligence for Engineering Design, Analysis and Manufacturing.
[9]
M. L. Maher,et al.
Using analogical reasoning to design buildings
,
2005,
Engineering with Computers.
[10]
Janine M. Benyus,et al.
Biomimicry: Innovation Inspired by Nature
,
1997
.
[11]
Amaresh Chakrabarti,et al.
The effect of representation of triggers on design outcomes
,
2008,
Artificial Intelligence for Engineering Design, Analysis and Manufacturing.
[12]
K. Dunbar.
HOW SCIENTISTS REALLY REASON: SCIENTIFIC REASONING IN REAL-WORLD LABORATORIES
,
1995
.
[13]
Ashok K. Goel,et al.
Innovation in Analogical Design: A Model-Based Approach
,
1994
.
[14]
Janet L. Kolodner,et al.
Towards More Creative Case-Based Design Systems
,
1994,
AAAI.
[15]
L. Darden,et al.
Reasoning Strategies in Molecular Biology: Abstractions, Scans and Anomalies
,
1994,
PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association.
[16]
Andrés Gómez de Silva Garza,et al.
Case-Based Reasoning in Design
,
1995,
IEEE Expert.
[17]
Arthur B. Markman,et al.
Modality and representation in analogy
,
2008,
Artificial Intelligence for Engineering Design, Analysis and Manufacturing.
[18]
Ashok K. Goel.
Design, Analogy, and Creativity
,
1997,
IEEE Expert.
[19]
John S. Gero,et al.
Function–behavior–structure paths and their role in analogy-based design
,
1996,
Artificial Intelligence for Engineering Design, Analysis and Manufacturing.
[20]
Ashok K. Goel,et al.
Biologically-Inspired Innovation in Engineering Design: a Cognitive Study
,
2007
.
[21]
David C. Brown,et al.
Knowledge and Control for a Mechanical Design Expert System
,
1986,
Computer.