Abstract
Our research on design adopts the perspectives of artificial intelligence, cognitive science, and human-centered computing. Thus, it produces information-processing theories, computational models, computer programs, and interactive tools for aspects of design. In this chapter, we describe these perspectives and products. We also illustrate some of the methods and artifacts of our research through a case study of problem–solution coevolution in biologically inspired design. Starting with the Structure-Behavior-Function knowledge model as a seed, we develop a knowledge model of design problems called SR.BID that is grounded in empirical data about biologically inspired design practice. SR.BID captures problem descriptions as well as problem–solution relationships in biologically inspired design, and thus forms the basis for the development of new interactive tools for supporting its practice as well as new pedagogical techniques for learning about problem formulation.
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Bar-Cohen Y (ed) (2011) Biomimetics: nature-based innovation. CRC Press, Boca Raton
Benyus J (1997) Biomimicry: innovation inspired by nature. William Morrow, New York
Bhatta S, Goel A (1997) Learning generic mechanisms for innovative strategies in adaptive design. J Learn Sci 6(4):367–396
Biomimicry 3.8 Institute (2008) AskNature. http://www.asknature.org/. Accessed on 25 Apr 2011
Blessing L, Chakrabarti A (2009) DRM: A design research methodology. Springer, London
Brown J, Collins A, Duguid P (1989) Situated cognition and the culture of learning. Educational Researcher 18(1):32–42
Chakrabarti A, Shu L (2010) Biologically inspired design. AIEDAM 24:453–454
Chandrasekaran B (1994) Functional representation: a brief historical perspective. Appl Artif Intell 8(2):173–197
Chandrasekaran B, Goel A, Iwasaki Y (1993) Functional representation as design rationale. IEEE Comput 26:48–56
Clancey W (1997) Situated cognition: on human knowledge and computer representations. Cambridge University Press, Cambridge
Clement J (2008) Creative model construction in scientists and students: the role of imagery, analogy, and mental simulation. Springer, Dordrecht
Darden L (1998) Anomaly-driven theory redesign: computational philosophy of science experiments. In: Bynum T, Moor J (eds) The digital phoenix: how computers are changing philosophy. Blackwell Publishers, New York, pp 62–78
Dinar M, Shaj J, Hunt G, Campana E, Langley P (2011) Towards a formal representational model of problem formulation in design. In: Proceedings of ASME 2011 IDETC/CIE Conference, Washington DC
Dorst K (2003) The problem of design problems. In: Cross N, Edmonds E (eds) Expertise in design. Creativity and Cognition Studio Press, Sydney
Dorst K, Cross N (2001) Creativity in the design process: co-evolution of problem-solution. Des Stud 22(5):425–437
Glaser B, Strauss A (1967) The discovery of grounded theory: strategies for qualitative research. Aldine, Chicago
Gebhardt F, Voß A, Gräther W, Schmidt-Belz B (1997) Reasoning with complex cases. Kluwer, Norwell, MA
Goel A (1992) Representation of design functions in experience-based design. In: Brown D, Waldron M, Yoshikawa H (eds) Intelligent computer aided design. North-Holland, Amsterdam, pp 283–308
Goel A, Bhatta S, Stroulia E (1997) Kritik: an early case-based design system. In: Maher M, Pu P (eds) Issues and applications of case-based reasoning in design. Mahwah, Erlbaum, pp 87–132
Goel A, Bhatta S (2004) Design patterns: a unit of analogical transfer in creative design. Adv Eng Inform 18(2):85–94
Goel A, Chandrasekaran B (1988). Integrating model-based reasoning with case based reasoning for design problem solving. In: Proceedings of AAAI-88 workshop on AI in design, Minneapolis
Goel A, Davies J (2011) Artificial Intelligence. In: Sternberg R, Kaufman S (eds) Cambridge handbook of intelligence, 3rd edn. Cambridge University Press, Cambridge
Goel A, Rugaber S, Vattam S (2009) Structure, behavior and function of complex systems: the SBF modeling language. Artif Intell Eng Des Anal Manuf 23:23–35
Goel A, Stroulia E (1996) Functional Device Models and Model-Based Diagnosis in Adaptive Design. Artif Intell Eng Des Anal Manuf 10:355–370
Goel A, Vattam S, Wiltgen B, 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. Comput Aided Des 44(10):879–900
Helms M (2011) Solution based problem evolution and problem inception in biologically inspired design. Technical report, GVU, Georgia Institute of Technology. GIT-GVU-11-10
Helms M, Goel A (2012) Analogical problem evolution in biologically inspired design. In: Proceedings fifth international conference on design computing and cognition, College Station, Texas, July 2012. Springer
Helms M, Vattam S, Goel A (2009) Biologically inspired design: product and processes. Des Stud 30(5):606–622
Kling R, Star SL (1998) Human centered systems in the perspective of organizational and social informatics. Comput Soc 28(1):22–29
Lamp J, Milton S (2007) Grounded theory as foundations for methods. In: Applied ontology, Proceedings of the 4th international conference on qualitative research in IT and IT in qualitative research (QualIT), Victoria University of Wellington
Langley P (2012) The cognitive systems paradigm. Adv Cognitive Sys 1:3–13
Machamer P, Darden L, Craver C (2000) Thinking about mechanisms. Philos Sci 67:1–25
Maher ML, Tang H (2003) Co-evolution as a computational and cognitive model of design. Res Eng Design 14(1):47–64
Nagel J, Nagel R, Stone R, McAdams D (2010) Function-based, biologically inspired concept generation. Artif Intell Eng Des Anal Manuf 24:521–535
Navinchandra D (1991) Exploration and innovation in design. Springer, New York
Nersessian N (2008) Creating scientific concepts. MIT Press, Cambridge, MA
Newell A, Simon H (1972) Human problem solving. Prentice-Hall, Englewood Cliffs
Prabhakar S, Goel A (1998) Functional modeling for enabling adaptive design of devices for new environments. Artif Intell Eng 12:417–444
Russell S, Norvig P (2010) Artificial intelligence: a modern approach, 3rd edn. PrenticeHall, Upper Saddle River
Sartori J, Pal U, Chakrabarti A (2010) A Methodology for supporting “transfer” in biomimetic design. AIEDAM 24:483–506
Shu L, Ueda K, Chiu I, Cheong H (2011) Biologically inspired design. CIRP Annals Manuf Technol 60:673–693
Strauss A, Corbin J (1990) Basics of qualitative research: grounded theory procedures and techniques. Sage, Thousand Oaks
Thagard P (2005) Mind: introduction to cognitive science, 2nd edn. MIT Press, Cambridge
Vattam S, Helms M, Goel A (2010) A content account of creative analogies in biologically inspired design. Artif Intell Eng Des Anal Manuf 24:467–481
Vincent J, Bogatyreva O, Bogatyrev N, Bowyer A, Pahl A (2006) Biomimetics: its practice and theory. J R Soc Interface 3:471–482
Vincent J, Mann D (2002) Systematic technology transfer from biology to engineering. Phil Trans R Soc Lond 360:156–173
Yen J, Weissburg M, Helms M, Goel A (2011) Biologically inspired design: a tool for interdisciplinary education. In: Bar-Cohen Y (ed) Biomimetics: nature-based innovation. Taylor & Francis, Boca Raton
Yen J, Weissburg M (2007) Perspectives on biologically inspired design: introduction to the collected contributions. J Bioinspir Biomim 2
Zhao F, Maher M (1988) Using analogical reasoning to design buildings. Engineering with Computers 4:107–122
Acknowledgments
We are grateful to the instructors and students of the ME/ISyE/MSE/PTFe/BIOL 4740 classes from 2006 through 2012, especially Professor Jeannette Yen, the main instructor and coordinator of the class. We are grateful also for the contributions of Marika Shahid, Swaroop Vattam, and Bryan Wiltgen. We thank the US National Science Foundation for its support of this research through an NSF CreativeIT Grant (0855916, Computational Tools for Enhancing Creativity in Biologically Inspired Engineering Design), and an NSF TUE Grant (1022778, Biologically !nspired Design: A Novel Interdisciplinary Biology-Engineering Curriculum.
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Appendix 1: Detailed Description of the SR.BID Knowledge Model
Appendix 1: Detailed Description of the SR.BID Knowledge Model
The following tables describe the ontology of the SR.BID knowledge model of design problems that emerged from analyzing problem statements in the Week 3 2010 and Week 8 2010 data sets. These tables refine the high-level ontology of concepts and relationships of Fig. 20.3.
Solution | Description |
---|---|
Primary type | |
Biological | The solution is a naturally occurring biological component, organism, or system |
Man-made | The designers refer to a system which someone already built or created, or for which they generated prototypes or specifications |
New design solution | The designers who are working on the problem are conjecturing a new design (or a design they think is new) to solve the problem |
Secondary type | |
Sub-solution | A sub-solution consists of many parts that together perform a specific function within the context of a larger solution |
Subtype | A subtype solution expresses a “kind-of” relationship with another solution |
Function | Description |
---|---|
Primary type | |
Accomplishment | The default function type, accomplishment functions change the state of the world in an intended way |
Preventative | Preventative functions keep a state OR another function from occurring |
Maintenance | Functions that maintain a state are considered maintenance for example “the thermostat regulates temperature” is a maintenance function |
Allow | Allow functions enable a state OR another function to occur |
Negation | Negative functions are stated as NOT performing another function, for instance this application does not produce light |
Secondary type | |
Sub-function, AND | When there are multiple sub-function relationships for a given function, AND-type relationships that specify that the related sub-functions must all be accomplished in order to achieve the parent function |
Sub-function, OR | When there are multiple sub-function relationships for a given function, the OR-type relationship specifies that one of the functions must be accomplished to achieve the parent function |
Operating environment | Description |
---|---|
Primary type | |
Location | The places in which the system is intended to operate |
Condition-qualitative | Qualitative conditions under which the system is intended to operate |
Condition-quantitative | High/low-end values, expected values, or ranges |
Time | The time during which the system must operate for example, “at night.” Words like “when,” “after,” “while,” “as,” and “during” are often used to express a temporal environment |
User | The phrase describes an intended user or class of users for the system |
Entity | The phrase describes an entity, often biological but sometimes technological, that interacts with the system |
System | The phrase describes another system within which the system is intended to work or connect |
Constraints and specifications | Description |
---|---|
Primary type | |
Material | The material of which one or more components of the design will be composed |
Information | Information can be in the form of energetic signals, bits and bytes, or may be encoded in the physical structure of a thing |
Energy | Energy can be found throughout a system in many forms; the energy subtype is used when a specified form of energy is discussed within the confines of the system |
Time | Includes timeframes not related to the operation of the design |
Component | Includes descriptions of specific parts of a solution or design, or groups of parts |
Property/value | Concerns the properties of the system as a whole or their values |
Shape | Includes the shape of the components or of the design |
Spatial orientation | These specify the spatial relationship or orientation between or among one or many components, systems, or subsystems |
Structural relationship | Any phrase specifying which components are related by means of connecting joints and contacts points |
Cost | Usually in monetary terms, but this could also be in terms of any resource of concern; absolute; or relative |
Secondary type | |
Limiting | Limiting specifications/constraints are those which require a designer to use a smaller subset of design elements |
Enabling | Enabling specifications/constraints offer new possibilities for design elements without enforcing their use |
Existing | Existing specifications/constraints discuss the specific properties of an existing design |
Performance criteria | Description |
---|---|
Primary type | |
Specific | States the specific value or range of the performance criteria |
Relative | Uses comparative terms such are “quieter than solution X,” without explicitly stating the performance of the compared to solution |
Actual | States the performance of an existing solution |
Deficiency/Benefit | Description |
---|---|
Primary type | |
Deficiency | Deficiencies can relate to any element of an existing solution or proposed design, highlighting an unfavorable aspect of that element |
Benefit | Benefits can relate to any element of an existing solution or proposed design, highlighting a favorable aspect of that element |
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Goel, A.K., Helms, M.E. (2014). Theories, Models, Programs, and Tools of Design: Views from Artificial Intelligence, Cognitive Science, and Human-Centered Computing. In: Chakrabarti, A., Blessing, L. (eds) An Anthology of Theories and Models of Design. Springer, London. https://doi.org/10.1007/978-1-4471-6338-1_20
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