Hostname: page-component-cd9895bd7-jn8rn Total loading time: 0 Render date: 2024-12-26T04:05:12.901Z Has data issue: false hasContentIssue false
  • English
  • Français

Evaluating the impact of Idea-Inspire 4.0 on analogical transfer of concepts

Published online by Cambridge University Press:  05 October 2018

L. Siddharth Open the ORCID record for L. Siddharth [Opens in a new window]  and
Amaresh Chakrabarti
L. Siddharth*
Affiliation:
Centre for Product Design and Manufacturing, Indian Institute of Science, Bengaluru, 560012, India
Amaresh Chakrabarti
Affiliation:
Centre for Product Design and Manufacturing, Indian Institute of Science, Bengaluru, 560012, India
*
Author for correspondence: L. Siddharth, E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

The biological domain has the potential to offer a rich source of analogies to solve engineering design problems. However, due to the complexity embedded in biological systems, adding to the lack of structured, detailed, and searchable knowledge bases, engineering designers find it hard to access the knowledge in the biological domain, which therefore poses challenges in understanding the biological concepts in order to apply these concepts to engineering design problems. In order to assist the engineering designers in problem-solving, we report, in this paper, a web-based tool called Idea-Inspire 4.0 that supports analogical design using two broad features. First, the tool provides access to a number of biological systems using a searchable knowledge base. Second, it explains each one of these biological systems using a multi-modal representation: that is, using function decomposition model, text, function model, image, video, and audio. In this paper, we report two experiments that test how well the multi-modal representation in Idea-Inspire 4.0 supports understanding and application of biological concepts in engineering design problems. In one experiment, we use Bloom's method to test “analysis” and “synthesis” levels of understanding of a biological system. In the next experiment, we provide an engineering design problem along with a biological-analogous system and examine the novelty and requirement-satisfaction (two major indicators of creativity) of resulting design solutions. In both the experiments, the biological system (analogue) was provided using Idea-Inspire 4.0 as well as using a conventional text-image representation so that the efficacy of Idea-Inspire 4.0 is tested using a benchmark.

Keywords

Analogical and bio-inspired designcreativity and ideationnoveltyrequirement-satisfactionunderstanding
Type
Research Article
Information
AI EDAM , Volume 32 , Special Issue 4: Design Creativity , November 2018 , pp. 431 - 448
Copyright
Copyright © Cambridge University Press 2018 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Anderson, LW, Krathwohl, DR and Bloom, BS (2001) A Taxonomy for Learning, Teaching, and Assessing: A Revision of Bloom's Taxonomy of Educational Objectives. New York, USA: Longman.Google Scholar
Boden, MA (1998) Creativity and artificial intelligence. Artificial Intelligence 103, 347–356.Google Scholar
Bohm, MR, Stone, RB and Szykman, S (2005) Enhancing virtual product representations for advanced design repository systems. Journal of Computing and Information Science in Engineering 5, 360–372.Google Scholar
Browning, TR (2001) Applying the design structure matrix to system decomposition and integration problems: a review and new directions. IEEE Transactions on Engineering Management 48, 292–306.Google Scholar
Chakrabarti, A (2005) A solution oriented approach to requirement identification. In: ICED 05: 15th International Conference on Engineering Design: engineering design and the global economy. Engineers Australia, pp. 3465-3477. https://search.informit.com.au/documentSummary;dn=401163166612932;res=IELENG.Google Scholar
Chakrabarti, A, Sarkar, P, Leelavathamma, B and Nataraju, B (2005) A functional representation for aiding biomimetic and artificial inspiration of new ideas. AI EDAM: Artificial Intelligence for Engineering Design, Analysis, and Manufacturing 19, 113–132.Google Scholar
Chakrabarti, A, Siddharth, L, Dinakar, M, Panda, M, Palegar, N and Keshwani, S (2017) Idea-Inspire 3.0 – a tool for analogical design. Presented at the International Conference on Research into Design (ICoRD'17). Springer Singapore, p. 10.Google Scholar
Cheong, H, Hallihan, GM and Shu, L (2014) Design problem solving with biological analogies: a verbal protocol study. AI EDAM: Artificial Intelligence for Engineering Design, Analysis and Manufacturing 28, 27–47.Google Scholar
Chiu, I and Shu, L (2007) Biomimetic design through natural language analysis to facilitate cross-domain information retrieval. AI EDAM: Artificial Intelligence for Engineering Design, Analysis, and Manufacturing 21, 45–59.Google Scholar
Croft, D, Coupland, S, Shell, J and Brown, S (2013) A fast and efficient semantic short text similarity metric. In: Computational Intelligence (UKCI), 2013 13th UK Workshop on. IEEE, pp. 221–227.Google Scholar
Engine, I (2015) This robotic hand is inspired by a chameleon's tongue.Google Scholar
Ethier, CR and Simmons, CA (2007) Introductory Biomechanics: From Cells to Organisms. Cambridge, UK: Cambridge University Press.Google Scholar
Fenves, SJ (2001) Core product model for representing design information. US Department of Commerce, Technology Administration, National Institute of Standards and Technology.Google Scholar
Fu, K, Murphy, J, Yang, M, Otto, K, Jensen, D and Wood, K (2015) Design-by-analogy: experimental evaluation of a functional analogy search methodology for concept generation improvement. Research in Engineering Design 26, 77–95.Google Scholar
Goel, AK, Vattam, S, Wiltgen, B and Helms, M (2012) Cognitive, collaborative, conceptual and creative – four characteristics of the next generation of knowledge-based CAD systems: a study in biologically inspired design. Computer-Aided Design 44, 879–900.Google Scholar
Gonçalves, M, Cardoso, C and Badke-Schaub, P (2012) Find your inspiration: exploring different levels of abstraction in textual stimuli. In: DS 73-1 Proceedings of the 2nd International Conference on Design Creativity Volume 1.Google Scholar
Gonçalves, M, Cardoso, C and Badke-Schaub, P (2016) Inspiration choices that matter: the selection of external stimuli during ideation. Design Science 2, e10. https://doi.org/10.1017/dsj.2016.10.Google Scholar
Helms, M, Vattam, S, Goel, AK and Yen, J (2011) Enhanced understand of biological systems using structure-behavior-function models. In: Advanced Learning Technologies (ICALT), 2011 11th IEEE International Conference on. IEEE, pp. 239–243.Google Scholar
Howard, TJ, Culley, SJ and Dekoninck, E (2008) Describing the creative design process by the integration of engineering design and cognitive psychology literature. Design Studies 29, 160–180.Google Scholar
Huijsen, W-O (1998) Controlled language – an introduction. In: Proceedings of CLAW. pp. 1–15.Google Scholar
Jansson, DG and Smith, SM (1991) Design fixation. Design studies 12, 3–11.Google Scholar
Keshwani, S and Chakrabarti, A (2016) Influence of analogical domains and comprehensiveness in explanation of analogy on the novelty of designs. Springer 1–30.Google Scholar
Keshwani, S and Chakrabarti, A (2017) Towards automatic classification of description of analogies into SAPPhIRE constructs. In: International Conference on Research into Design. Springer, pp. 643–655.Google Scholar
Kindt, TJ, Goldsby, RA, Osborne, BA and Kuby, J (2007) Kuby Immunology. New York: W.H. Freeman.Google Scholar
Kuhn, T (2014) A survey and classification of controlled natural languages. Computational Linguistics 40, 121–170.Google Scholar
Lepionka, ME (2008) Writing and Developing Your College Textbook: A Comprehensive Guide to Textbook Authorship and Higher Education Publishing. Gloucester, MA: Atlantic Path Publishing.Google Scholar
Linsey, J, Wood, K and Markman, A (2008) Modality and representation in analogy. AI EDAM: Artificial Intelligence for Engineering Design, Analysis and Manufacturing 22, 85–100.Google Scholar
Linsey, JS, Clauss, E, Kurtoglu, T, Murphy, J, Wood, K and Markman, A (2011) An experimental study of group idea generation techniques: understanding the roles of idea representation and viewing methods. Journal of Mechanical Design 133, 031008.Google Scholar
Mak, T and Shu, L (2008) Using descriptions of biological phenomena for idea generation. Research in Engineering Design 19, 21–28.Google Scholar
Merriam-Webster (2004) Merriam-Webster's Collegiate Dictionary. Springfield, MA: Merriam-Webster.Google Scholar
Miller, GA (1995) Wordnet: a lexical database for English. Communications of the ACM 38, 39–41.Google Scholar
Murphy, J, Fu, K, Otto, K, Yang, M, Jensen, D and Wood, K (2014) Function based design-by-analogy: a functional vector approach to analogical search. Journal of Mechanical Design 136, 101102.Google Scholar
Nagel, RL, Midha, PA, Tinsley, A, Stone, RB, McAdams, DA and Shu, L (2008) Exploring the use of functional models in biomimetic conceptual design. Journal of Mechanical Design 130, 121102.Google Scholar
Pack, RT, Iskarous, MZ and Kawamura, K (1996) Climber robot. US Patent Database, US5551525A, Vanderbilt University, Nashville, Tenn, pp. 1–17.Google Scholar
Pahl, G and Beitz, W (2007) Engineering Design: A Systematic Approach, Springer. London: Springer-verlag.Google Scholar
Pimmler, TU and Eppinger, SD (1994) Integration analysis of product decompositions. Massachusetts Institute of Technology (MIT), Sloan School of Management, Massachusetts Ave, Cambridge, pp. 1–10.Google Scholar
Qian, L and Gero, JS (1996) Function–behavior–structure paths and their role in analogy-based design. Artificial Intelligence for Engineering, Design, Analysis and Manufacturing 10, 289–312.Google Scholar
Natural Reader (2017) NaturalReader. Vancouver, BC Canada. https://www.naturalreaders.com/.Google Scholar
Sarkar, P and Chakrabarti, A (2008) The effect of representation of triggers on design outcomes. AI EDAM: Artificial Intelligence for Engineering Design, Analysis and Manufacturing 22, 101.Google Scholar
Sarkar, P and Chakrabarti, A (2011) Assessing design creativity. Design Studies 32, 348–383.Google Scholar
Sarkar, P and Chakrabarti, A (2015) Creativity: generic definition, tests, factors and methods. International Journal of Design Sciences and Technology 21, 7–37.Google Scholar
Sarkar, P, Phaneendra, S and Chakrabarti, A (2008) Developing engineering products using inspiration from nature. Journal of Computing and Information Science in Engineering 8, 031001.Google Scholar
Schwitter, R (2010) Controlled natural languages for knowledge representation. In: Proceedings of the 23rd International Conference on Computational Linguistics: Posters. Association for Computational Linguistics, pp. 1113–1121.Google Scholar
Shah, JJ, Smith, SM and Vargas-Hernandez, N (2003) Metrics for measuring ideation effectiveness. Design Studies 24, 111–134.Google Scholar
Stone, RB and Wood, KL (2000) Development of a functional basis for design. Journal of Mechanical Design 122, 359–370.Google Scholar
Ulrich, K (1995) The role of product architecture in the manufacturing firm. Research Policy 24, 419–440.Google Scholar
Umeda, Y, Takeda, H, Tomiyama, T and Yoshikawa, H (1990) Function, behaviour, and structure. Applications of Artificial Intelligence in Engineering V 1, 177–193.Google Scholar
Vattam, SS, Helms, ME and Goel, AK (2010) A content account of creative analogies in biologically inspired design. AI EDAM: Artificial Intelligence for Engineering Design, Analysis and Manufacturing 24, 467–481.Google Scholar
Verhaegen, P-A, Peeters, J, Vandevenne, D, Dewulf, S and Duflou, JR (2011) Effectiveness of the PAnDA ideation tool. Procedia Engineering 9, 63–76.Google Scholar
Ware, C (2010) Visual Thinking: for Design. Burlington, MA: Morgan Kaufmann.Google Scholar
Wiltgen, B, Vattam, S, Helms, M, Goel, AK and Yen, J (2011) Learning functional models of biological systems for biologically inspired design. In: Advanced Learning Technologies (ICALT), 2011 11th IEEE International Conference on. IEEE, pp. 355–357.Google Scholar
Yargin, GT and Crilly, N (2015) Information and interaction requirements for software tools supporting analogical design. AI EDAM: Artificial Intelligence for Engineering Design, Analysis, and Manufacturing 29, 203–214.Google Scholar