Hostname: page-component-7b7567c8c-wxvwp Total loading time: 0 Render date: 2026-11-15T15:09:55.639Z Has data issue: false hasContentIssue false
  • English
  • Français

Innovative design Systems, where are we and where do we go from here? Part I: Design by association

Published online by Cambridge University Press:  07 July 2009

D. Navin Chandra
D. Navin Chandra
Affiliation:
School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA
Get accessRights & Permissions [Opens in a new window]

Abstract

Designing is a skill central to many human tasks. Designers are constantly producing newer and better artifacts, generating innovative solutions to problems in our world. This paper looks at innovation, and research that is aimed at developing theories and methodologies for innovative design. We view design as a process of association and exploration. These two approaches are fundamental to innovation. The aim of exploration is to generate a large variety of design alternatives by breaking away from the norms by looking in unlikely places, and by relaxing binding constraints. Exploration exposes possibilities that would not normally hâve been considered, possibilities that may serendipitously lead to innovative solutions. Association, on the other hand, attempts to exploit previous design experiences in a new design context. This is donc by recognizing useful analogies that can help in synthesizing parts of a design, recognizing unforeseen problems, and discovering opportunities. This paper is the first part of a two-part paper that presents and discusses a variety of association and exploration methods. This part examines association-based techniques, some of which have been used in actual design Systems, and others that point to the solution of some open questions in design research. We develop these ideas by examining connections between design research and other disciplines such as cognitive psychology, artificial intelligence, the history of science, and the creativity literature.

Information

Type
Research Article
Information
The Knowledge Engineering Review , Volume 7 , Issue 3 , September 1992, pp. 183 - 213
Copyright
Copyright © Cambridge University Press 1992

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.)

Article purchase

Temporarily unavailable

References

Bourne, D, Navin chandra, D and Ramaswamy, R, 1989. “Relative tolerances and kinematic behaviour” In: Gero, J, editer, Al in Design, Computational Mechanics.Google Scholar
Brown, DC, and Chandrasekaran, B, 1984. “Expert Systems for a class of mechanical design activity” In: Proceedings of IFIP W. G. 5.2 Working Conference on Knowledge Engineering in Computer-Aided Design,Budapest, Hungary.Google Scholar
Brown, DC and Chandrasekaran, B, 1986. “Expert Systems for a class of mechanical design activity” In: D Sriram and B Adey, editors, Proceedings of the First International Conference on Al applications in Engineering,Computational Mechanics.Google Scholar
Carbonnell, JG, 1983. “Learning by analogy: Formulating and generalizing plans from past experience” In: Michalski, RS, Carbonell, JG and Mitchell, TM, editors, Machine Learning: An Artificial Intelligence Approach, Tioga Press.Google Scholar
Carbonnell, JG, 1983b. “Deprivational analogy and its role in problem solving” In: Proceedings of AAAI-83, pp 64–69.Google Scholar
Carbonnell, JG, 1986. “Deprivational analogy: A theory of reconstructive problem solving and expertise acquisition” In: Michalski, RS, Carbonell, JG and Mitchell, TM, editors, Machine Learning: An Artificial Intelligence Approach Vol 2, Morgan Kaufman.Google Scholar
Dixon, JR, 1985. Design Engineering: Inventiveness, Analysis and Decision Making McGraw Hill.Google Scholar
Dixon, JR, 1988. “Designing with features: Building manufacturing knowledge into more intelligent CAD Systems” In: Proceedings of ASME Manufacturing lnternational-88 Atlanta GA, 04 17–20.Google Scholar
Dyer, MG, Flowers, M and Hodges, J, 1986. “EDISON: An Engineering Design Invention System Operating Naively” In: Proceedings of the First International Conference on Applications of Al to Engineering.CrossRefGoogle Scholar
Evans, TG. “A program for the solution of a class of geometric analogy intelligence test questions” In: Minsky, M, editor, Semantic Information Processing MIT Press.Google Scholar
Faltings, B, 1989. “Qualitative kinematics in mechanisms” Artificial Intelligence (accepted).CrossRefGoogle Scholar
Fikes, RE, 1969. REF-ARF: A System For Solving Problems Related as Procedures Technical Report, Technical Note 14, Stanford Research Institute.Google Scholar
Fikes, RE and Nilsson, NJ, 1971. “STRIPS: A new approach to the application of theorem proving to problem solving” Artificial Intelligence 2 189–208.CrossRefGoogle Scholar
Fox, MS, 1983. Constraint Directed Search: A case of Job Shop Scheduling PhD thesis, Carnegic-Mellon University.Google Scholar
Gentner, D, 1983. “Structure mapping: A theoretical framework for analogy” Cognitive Science 7.CrossRefGoogle Scholar
Gentner, D and Landers, R, 1985. “Analogical reminding: A good match is hard to find” In: Proceedings of the International Conference on Systems, Mon and Cybernetics pp 607–613, Tucson, AZ.Google Scholar
Gentner, D and Toupin, C, 1986. “Systematicity and surface similarity in the development of analogy” Cognitive Science 10 277–300.CrossRefGoogle Scholar
Gero, J, 1987. Seminar at Carnegie Mellon University.Google Scholar
Gero, J, 1990. “Design prototypes: A knowledge representation schema for design” AI Magazine 11(4) 26–36.Google Scholar
Goel, AK, 1989. Integration of Case-Based Reasoning and Model-Based Reasoning for Adaptive Design Problem Solving PhD thesis, Ohio State UniversityGoogle Scholar
Goel, AK, Kolodner, JL, Pearce, M, Billington, R and Zimring, C, 1991. “Towards a case-based tool for aiding conceptual design problem solving” In: Proceedings of the 1991 DARPA workshop on Case Based Reasoning, pp 109–120.Google Scholar
Gordon, WJ, 1961. Synectics: The development of Creative Capacity Harper & Row.Google Scholar
Gross, MD, 1986, Design as Exploring Constraints PhD thesis, MIT.Google Scholar
Guilford, JP, 1959. “Creativity” American Psychologist (5) 444–454.CrossRefGoogle Scholar
Hammond, KJ, 1986. “CHEF: A model of case-based planning” In: Proceedings of AAAI-86 pp 267–271, Philadelphia, PA.Google Scholar
Holland, JH, 1975. Adaptation in natural and artificial Systems University of Michigan Press.Google Scholar
Holyoak, KJ and Koh, K, 1987. “Surface and structural similarity in analogical transfer” Memory and Cognition 15 332–340.CrossRefGoogle ScholarPubMed
Huhns, MH, and Acosta, RD, 1987. Argo: An Analogical Reasoning System for Solving Design Problems, Technical Report AI/CAD-092–87, Microelectronic and Computer Technology Corporation.Google Scholar
Jevons, WS, 1892. The Principles of Science McMillan.Google Scholar
Joskowicz, L and Addanki, S, 1988. “From kinematics to shape: An approach to innovative design”, In: Proceedings of the Seventh National Conference on Artificial Intelligence pp. 347–352.Google Scholar
Kass, AM and Leake, DB, 1988. “Case-based reasoning applied to constructing explanations” In Proceedings of the DARPA Workshop on Case-based Reasoning pp 190–208.Google Scholar
Kedar-Cabelli, ST, 1985a. Analogy—From a unified perspective Technical Report ML-TR-3, Department of Computer Science, Rutgers University.Google Scholar
Kedar-Cabelli, ST, 1985b. “Purpose-directed analogy” In: Proceedings of the Cognitive Science Society Conference.Google Scholar
Kedar-Cabelli, ST, 1985c. Analogy—From a unified perspective Technical Report ML-TR-3, Rutgers University.Google Scholar
Kling, RE, 1971. “A paradigm for reasoning by analogy” Artificial Intelligence 2(2).CrossRefGoogle Scholar
Koestler, A, 1984. The act of creation McMillan.Google Scholar
Kolodner, JL, 1980. Retrieval and organizational strategies in conceptual memory: A computer model PhD thesis, Yale University.Google Scholar
Kolodner, JL, 1981. “Organization and retrieval in a conceptual memory for events” In: Proceedings of the Seventh International Joint Conference on Artificial Intelligence.Google Scholar
Kolodner, JL, 1984. Retrieval and Organizational Strategies in Conceptual Memory: A Computer Model Lawrence Erlbaum Associates.Google Scholar
Kolodner, JL, 1988. “Retrieving events from a case memory: A parallel implementation” In: Proceedings of the 1988 Case-Based Reasoning Workshop pp 233–249, Clearwater, FL.Google Scholar
Kota, S, 1990. “A qualitative matrix representation scheme for the conceptual design of mechanisms” In: Proceedings of the ASME Design Automation Conference (21st Biannual ASME Mechanisms Conference).Google Scholar
Kuhn, TS, 1970. The structure of scientific revolutions University of Chicago Press.Google Scholar
Lozano-Perez, T, 1983. “Spatial planning: A configuration space approach” IEEE Transactions on Computers C-32(2).CrossRefGoogle Scholar
Maher, ML, 1984. HI-RISE: An Expert System For The Preliminary Structural Design of High Rise Buildings PhD thesis, Department of Civil Engineering, Carnegie-Mellon University.Google Scholar
Maher, ML and Zhao, F, 1986. Using experience to plan the synthesis of new designs Technical Report, Engineering Design Research Center, CMU.Google Scholar
Maher, ML and Zhao, F, 1987. “Using experience to plan the synthesis of new designs” In: Gero, JS, editer, Expert Systems in Computer-Aided Design North-Holland.Google Scholar
Minski, M, 1982. Learning Meaning Technical Report, M.I.T.Google Scholar
Mitchell, TM, Keller, RM and Kedar-Cabelli, ST, 1986, “Explanation-based generalization: A unifying view” Machine Learning 1(1).CrossRefGoogle Scholar
Mittal, S, Dym, C and Morjaria, M, 1985. “PRIDE: An expert System for the design of paper handling Systems” In: Dym, C, editor, Applications of Knowledge-Based Systems to Engineering Analysis and Design pp 99–116, American Society of Mechanical Engineers.Google Scholar
Mostow, J, 1985. “Toward better models of the design process” The AI Magazine Spring.Google Scholar
Mostow, J, 1986. “Why are design derivations hard to replay?” In: Mitchell, TM, Carbonell, JG and Michalski, RS, editors, Machine Learning—A guide to carrent research Kluwer.Google Scholar
Murthy, SS and Addanki, S, 1987. “PROMPT: An innovative design tool” Fn: Proceedings of the sixth national conference on artificial intelligence pp 637–642.Google Scholar
Navin chandra, D, 1987. Exploring for Innovative Designs by Relaxing Criteria and reasoning from Precedent- Based Knowledge PhD thesis, MIT.Google Scholar
Navin chandra, D, 1988. “Case-based reasoning in CYCLOPS, a design problem solver” In: Kolodner, J, editor, Proceedings ofthe DARPA Workshop on Case-based Reasoning pp 286–301, Morgan Kaufman.Google Scholar
Navin chandra, D, 1991. Exploration and Innovation in Design: Towards a Computational Model Springer-Verlag.CrossRefGoogle Scholar
Navin chandra, D, Sriram, D and Kedar-Cabelli, ST, 1987. “On the role of analogy in engineering design: An overview” In: D Sriram and B Adey, editor, AI in Engineering, Proceedings of the 2nd International ConferenceComputational Mechanics.Google Scholar
Navin chandra, D, Sycara, K and Narasimhan, S, 1991a. “A transformational approach to case based synthesis” AI EDAM 5(1).Google Scholar
Navin chandra, D, Sycara, KP and Narasimhan, S, 1991b. “Behavioural synthesis in CADET, A case-based design tool” In: Proceedings ofthe Seventh Conference on Artificial Intelligence ApplicationsMiami, FL.Google Scholar
Newell, A and Simon, HA, 1972. Human Problem Solving, Prentice-Hall.Google Scholar
Nilsson, NJ, 1980. Principles of Artificial Intelligence, Tioga Publishing Co.Google Scholar
Osborn, AF, 1953. Applied Imagination Charles Scribner's Sons.Google Scholar
Pahl, G and Beitz, W, 1984. Engineering Design, Springer-Verlag.Google Scholar
Prieditis, A, editor, 1988. Analogica Pitman.Google Scholar
Rickards, T, 1974. Problem Solving through Creative Analysis Wiley.Google Scholar
Riesbeck, CK and Schank, R, 1989. Inside Case-Based Reasoning Lawrence Erlbaum Associates.Google Scholar
Rosenman, M and Gero, JS, 1989. “Creativity in design using a prototype approach” In: Proceedings of the International Conference on Creative Design pp 207–232.Google Scholar
Rossman, J, 1931. The Psychology ofthe Inventer Inventor's Publishing.Google Scholar
Schank, RC, 1982. Dynamic Memory: A Theory of reminding and learning in computers and people Cambridge University Press.Google Scholar
Schank, RC, 1986. Explanation Patterns: Understanding Mechanically and Creatively Lawrence Erlbaum Associates.Google Scholar
Schank, R, 1988. The Creative Attitude: Learning to ask and answer the right questions Erlbaum.Google Scholar
Schank, RC and Abelson, RP, 1977. Scripts, Plans, Goals and Understanding Lawrence Erlbaum Associates.Google Scholar
Sriram, D, 1986. Knowledge-Based Approaches for Structural Design PhD thesis, Carnegie Mellon University.Google Scholar
Stefik, M, 1980. Planning with Constraints PhD thesis, Stanford University.Google Scholar
Steinberg, L, Langrana, N, Mitchell, T, Mostow, J and Tong, C, 1986. A Domain Independent Model of Knowledge-Based Design Technical Report AI/VLSI Project Working Paper No. 33, Rutgers University.Google Scholar
Sussman, GJ and Steele, GL, 1980. “CONSTRAINTS—A language for expressing almost hierarchical constraints” Artificial Intelligence (14) 1–39.CrossRefGoogle Scholar
Sycara, K, 1987. Resolving Adversarial Conflicts: An Approach Integrating Case-Based and Analytic Methods PhD thesis, School of Information and Computer Science Georgia Institute of Technology.Google Scholar
Sycara, K and Navin chandra, D, 1989a. “Representing and indexing design cases” In: Proceedings of the Second International Conference on Industrial and Engineering Applications of AI and Expert SystemsTullahomK, TN.CrossRefGoogle Scholar
Sycara, D and Navin chandra, D, 1989b. “Integrating case-based reasoning and qualitative reasoning in design” In: Gero, J, editor, AI in Design Computational Mechanics.Google Scholar
Sycara, K and Navin chandra, D, 1991. “Index transformation techniques for facilitating creative use of multiple cases” In: Proceedings of the twelth International Joint Conference on Artificial Intelligence.Google Scholar
Tong, C, 1986a. Knowledge-Based Circuit Design PhD thesis, Stanford University.Google Scholar
Tong, C, 1986b. A framework for organizing and evaluating knowledge-based models of the design process Technical Report AI/VLSI Project Working Paper No. 21, Rutgers University.Google Scholar
Tversky, A, 1977. “Features of similarity” Psychology Review 84(4), 07.CrossRefGoogle Scholar
Ullman, DG and Dietterich, TA, 1987. “Mechanical design methodology: Implications on future developments of computer-aided design and knowledge-based Systems” Engineering with Computers 2 21–29.CrossRefGoogle Scholar
Wallas, G, 1926. The Art of Thought Harcourt.Google Scholar
Williams, B, 1989. Invention from First Principles via Topologies of Interaction PhD thesis, Massachusetts Institute of Technology.Google Scholar
Williams, B, 1990. “Interaction-based invention: Designing novel devices from first principles” In: Proceedings of AAAI-90 pp 349–356, Boston, MA.Google Scholar
Winston, PH, 1980. “Learning and reasoning by analogy” Communications of the ACM 23(12), 12.CrossRefGoogle Scholar
Winston, PH, 1981. Learning New Principles from Precedents and Exercises: The Details Technical Report AI Lab Memo 632, MIT AI Lab.Google Scholar
Winston, PH, Binford, TO, Katz, B and Lowry, M, 1983. “Learning physical descriptions from functional definitions, examples and precedents” In: Proceedings of AAAI-83 08.Google Scholar
Woodbury, RF, 1989. “Design Genes” In: Proceedings of the International Conference on Creative Design pp 133–154Google Scholar