Hostname: page-component-cd9895bd7-gxg78 Total loading time: 0 Render date: 2024-12-26T09:27:43.209Z Has data issue: false hasContentIssue false

Interpretation-driven mapping: A framework for conducting search and rerepresentation in parallel for computational analogy in design

Published online by Cambridge University Press:  27 April 2015

Kazjon Grace*
Affiliation:
College of Computing and Informatics, University of North Carolina at Charlotte, Charlotte, North Carolina, USA
John Gero
Affiliation:
College of Computing and Informatics, University of North Carolina at Charlotte, Charlotte, North Carolina, USA Krasnow Institute for Advanced Study, George Mason University, Washington, District of Columbia, USA
Rob Saunders
Affiliation:
Faculty of Architecture, Design and Planning, University of Sydney, Sydney, Australia
*
Reprint requests to: Kazjon Grace, College of Computing and Informatics, University of North Carolina at Charlotte, Charlotte, NC 28223, USA. E-mail: k.grace@uncc.edu

Abstract

This paper presents a framework for the interactions between the processes of mapping and rerepresentation within analogy making. Analogical reasoning systems for use in design tasks require representations that are open to being reinterpreted. The framework, interpretation-driven mapping, casts the process of constructing an analogical relationship as requiring iterative, parallel interactions between mapping and interpreting. This paper argues that this interpretation-driven approach focuses research on a fundamental problem in analogy making: how do the representations that make new mappings possible emerge during the mapping process? The framework is useful for both describing existing analogy-making models and designing future ones. The paper presents a computational model informed by the framework Idiom, which learns ways to reinterpret the representations of objects as it maps between them. The results of an implementation in the domain of visual analogy are presented to demonstrate its feasibility. Analogies constructed by the system are presented as examples. The interpretation-driven mapping framework is then used to compare representational change in Idiom to that in three previously published systems.

Type
Special Issue Articles
Copyright
Copyright © Cambridge University Press 2015 

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

REFERENCES

Barnden, J.A., & Holyoak, K.J. (1994). Advances in Connectionist and Neural Computation Theory: Analogy, Methaphor, and Reminding. New York: Ablex.Google Scholar
Bhatta, S.R., & Goel, A. (1997). Learning generic mechanisms for innovative strategies in adaptive design. Journal of the Learning Sciences 6(4), 367396.Google Scholar
Chalmers, D.J., French, R.M., & Hofstadter, D.R. (1992). High-level perception, representation, and analogy: a critique of artificial intelligence methodology. Journal of Experimental & Theoretical Artificial Intelligence 4(3), 185211.Google Scholar
Clancey, W.J. (1997). Situated Cognition: On Human Knowledge and Computer Representations. New York: Cambridge University Press.Google Scholar
Cliff, S. (1998). The English Archive of Design and Decoration. London: Thames & Hudson.Google Scholar
Cottingham, L.N. (1824). The Smith and Founder's Director: Containing a Series of Designs and Patterns for Ornamental Iron and Brass Work. London: Hullmandel.Google Scholar
Cross, A.D., Wilson, R.C., & Hancock, E.R. (1996). Genetic search for structural matching. Proc. Computer Vision ECCV'96, pp. 514525. New York: Springer.Google Scholar
Davies, J., & Goel, A.K. (2001). Visual analogy in problem solving. Proc. 17th Int. Joint Conf. Artificial Intelligence, Vol. 1, pp. 377–382. San Diego, CA: Morgan Kaufmann.Google Scholar
Davies, J., & Goel, A.K. (2003). Representation issues in visual analogy. Proc. 25th Annual Conf. Cognitive Science Society. Mahwah, NJ: Erlbaum.Google Scholar
Davies, J., Goel, A.K., & Nersessian, N.J. (2003). Visual re-representation in creative analogies. Proc. 3rd Workshop on Creative Systems: Int. Joint Conf. Artificial Intelligence, pp. 1–12. Mahwah, NJ: Erlbaum.Google Scholar
Detterman, D.K., & Sternberg, R.J. (1993). Transfer on Trial: Intelligence, Cognition, and Instruction. New York: Ablex.Google Scholar
Doumas, L.A., Hummel, J.E., & Sandhofer, C.M. (2008). A theory of the discovery and predication of relational concepts. Psychological Review 115(1), 143.Google Scholar
Evans, T. (1964). A heuristic program to solve geometric-analogy problems. Proc. 1964 Spring Joint Computer Conf., pp. 327–338. New York: ACM Press.Google Scholar
Falkenhainer, B. (1990). Analogical interpretation in context. Proc. 12th Annual Conf. Cognitive Science Society, pp. 69–76. Austin, TX: Cognitive Science Society.Google Scholar
Falkenhainer, B., Forbus, K.D., & Gentner, D. (1986). The structure-mapping engine. University of Illinois at Urbana-Champaign, Department of Computer Science.Google Scholar
Falkenhainer, B., Forbus, K.D., & Gentner, D. (1989). The structure-mapping engine: algorithm and examples. Artificial Intelligence 41(1), 163.Google Scholar
Fauconnier, G., & Turner, M. (2003). The Way We Think: Conceptual Blending and the Mind's Hidden Complexities. New York: Basic Books.
Forbus, K.D., Ferguson, R.W., & Gentner, D. (1994). Incremental structure-mapping. Proc. 16th Annual Conf. Cognitive Science Society, pp. 313–318. Hillsdale, NJ: Erlbaum.Google Scholar
Forbus, K.D., Gentner, D., & Law, K. (1995). MAC/FAC: a model of similarity-based retrieval. Cognitive Science 19(2), 141205.Google Scholar
Forbus, K., Usher, J., Lovett, A., Lockwood, K., & Wetzel, J. (2011). Cogsketch: sketch understanding for cognitive science research and for education. Topics in Cognitive Science 3(4), 648666.Google Scholar
French, R. (2002). The computational modeling of analogy-making. Trends in Cognitive Sciences 6(5), 200205.Google Scholar
Garey, M.R., & Johnson, D.S. (1979). Computers and Intractability: A Guide to NPCompleteness. New York: W.H. Freeman.Google Scholar
Gentner, D. (1983). Structure-mapping: a theoretical framework for analogy. Cognitive Science 7(2), 155170.Google Scholar
Gentner, D., & Forbus, K.D. (2011). Computational models of analogy. Wiley Interdisciplinary Reviews: Cognitive Science 2(3), 266276.Google Scholar
Gentner, D., & Holyoak, K.J. (1997). Reasoning and learning by analogy: introduction. American Psychologist 52(1), 32.Google Scholar
Gero, J.S. (1998). Conceptual designing as a sequence of situated acts. In Artificial Intelligence in Structural Engineering, pp. 165177. New York: Springer.Google Scholar
Gick, M.L., & Holyoak, K.J. (1980). Analogical problem solving. Cognitive Psychology 12(3), 306355.Google Scholar
Gick, M.L., & Holyoak, K.J. (1983). Schema induction and analogical transfer. Cognitive Psychology 15(1), 138.Google Scholar
Griffith, T.W., Nersessian, N.J., & Goel, A.K. (1996). The role of generic models in conceptual change. Proc. 18th Annual Conf. Cognitive Science Society, pp. 312–317. Mahwah, NJ: Erlbaum.Google Scholar
Griffith, T.W., Nersessian, N.J., & Goel, A. (2000). Function-follows-form transformations in scientific problem solving. Proc. 22nd Annual Conf. Cognitive Science Society, pp. 196–201. Mahwah NJ: Erlbaum.Google Scholar
Hahn, U., Chater, N., & Richardson, L.B. (2003). Similarity as transformation. Cognition 87(1), 132.Google Scholar
Hall, R.P. (1989). Computational approaches to analogical reasoning: a comparative analysis. Artificial Intelligence 39(1), 39120.Google Scholar
Harpaz-Itay, Y., Kaniel, S., & Ben-Amram, E. (2006). Analogy construction versus analogy solution, and their influence on transfer. Learning and Instruction 16(6), 583591.Google Scholar
Hodgetts, C.J., Hahn, U., & Chater, N. (2009). Transformation and alignment in similarity. Cognition 113(1), 6279.Google Scholar
Hofstadter, D. (1984). The Copycat project: an experiment in nondeterminism and creative analogies. MIT Artificial Intelligence Laboratory AI Memo 755. Cambridge, MA: MIT.Google Scholar
Hofstadter, D.R. (2008). Fluid Concepts and Creative Analogies: Computer Models of the Fundamental Mechanisms of Thought. New York: Basic Books.Google Scholar
Hofstadter, D.R., & Mitchell, M. (1992). An overview of the Copycat project. In Connectionist Approaches to Analogy, Metaphor, and Case-Based Reasoning (Holyoak, K., & Barnden, J., Eds.). New York: Ablex.Google Scholar
Holyoak, K.J. (2012). Analogy and relational reasoning. The Oxford Handbook of Thinking and Reasoning, pp. 234259. Oxford: Oxford University Press.Google Scholar
Holyoak, K.J., Novick, L.R., & Melz, E.R. (1994). Component Processes in Analogical Transfer: Mapping, Pattern Completion, and Adaptation. New York: Ablex.Google Scholar
Holyoak, K.J., & Thagard, P. (1989). Analogical mapping by constraint satisfaction. Cognitive Science 13(3), 295355.Google Scholar
Humbert, C. (1970). Ornamental Design: Europe, Africa, Asia, the Americas, Oceania: A Source Book with 1000 Illustrations. London: Thames & Hudson.Google Scholar
Kann, V. (1992). On the approximability of NP-complete optimization problems. PhD Thesis. Royal Institute of Technology Stockholm.Google Scholar
Koestler, A. (1967). The Act of Creation. New York: Penguin Books.Google Scholar
Kokinov, B., & Petrov, A. (2001). Integrating memory and reasoning in analogy-making: the AMBR model. In The Analogical Mind: Perspectives from Cognitive Science. Cambridge, MA: MIT Press.Google Scholar
Lakoff, G., & Johnson, M. (2003). Metaphors We Live By, 2nd ed.Chicago: University of Chicago Press.Google Scholar
Lovett, A., Gentner, D., Forbus, K., & Sagi, E. (2009). Using analogical mapping to simulate time-course phenomena in perceptual similarity. Cognitive Systems Research 10(3), 216228.Google Scholar
Lovett, A., Tomai, E., Forbus, K., & Usher, J. (2009). Solving geometric analogy problems through two-stage analogical mapping. Cognitive Science 33(7), 11921231.Google Scholar
Mahon, B.Z., & Caramazza, A. (2008). A critical look at the embodied cognition hypothesis and a new proposal for grounding conceptual content. Journal of Physiology (Paris) 102(1), 5970.Google Scholar
McDermott, J. (1979). Learning to use analogies. Proc. 6th International Joint Conference Artificial Intelligence, Vol. 1, pp. 568–576. San Francisco, CA: Morgan Kaufmann.Google Scholar
Penn, D.C., Holyoak, K.J., & Povinelli, D.J. (2008). Darwin's mistake: explaining the discontinuity between human and nonhuman minds. Behavioral and Brain Sciences 31(2), 109130.Google Scholar
Petkov, G., Vankov, I., & Kokinov, B. (2011). Unifying deduction, induction, and analogy by the AMBR model. Proc. 33rd Annual Conf. Cognitive Science Society. Hillsdale, NJ: Erlbaum.Google Scholar
Qian, L., & Gero, J.S. (1996). Function–behavior–structure paths and their role in analogy-based design. Artificial Intelligence for Engineering, Design Analysis and Manufacturing 10(4), 289312.Google Scholar
Ramscar, M., & Yarlett, D. (2003). Semantic grounding in models of analogy: an environmental approach. Cognitive Science 27(1), 4171.Google Scholar
Robertson, I. (2000). Imitative problem solving: why transfer of learning often fails to occur. Instructional Science 28(4), 263289.Google Scholar
Schacter, D.L., Norman, K.A., & Koutstaal, W. (2000). The cognitive neuroscience of constructive memory. In False-Memory Creation in Children and Adults: Theory, Research, and Implications (Bjorklund, D.F., Ed.), pp. 129168. London: Taylor & Francis.Google Scholar
Sowa, J.F. & Majumdar, A.K. (2003). Analogical reasoning. In Conceptual Structures for Knowledge Creation and Communication, pp. 1636. Dresden, Germany: Springer.Google Scholar
Spellman, B.A., & Holyoak, K.J. (1992). If Saddam is Hitler then who is George Bush? Analogical mapping between systems of social roles. Journal of Personality and Social Psychology 62(6), 913.Google Scholar
Tan, K.-L., Ooi, B.C., & Thiang, L.F. (2003). Retrieving similar shapes effectively and efficiently. Multimedia Tools and Applications 19(2), 111134.Google Scholar
Turney, P.D. (2008). The latent relation mapping engine: algorithm and experiments. Journal of Artificial Intelligence Research (JAIR) 33, 615655.Google Scholar
Visser, W. (1996). Two functions of analogical reasoning in design: a cognitive-psychology approach. Design Studies 17(4), 417434.CrossRefGoogle Scholar
Wang, T., & Zhou, J. (1997). Emcss: a new method for maximal common substructure search. Journal of Chemical Information and Computer Sciences 37(5), 828834.Google Scholar
Wolstencroft, J. (1989). Restructuring, reminding and repair: what's missing from models of anology. AI Communications 2(2), 5871.Google Scholar
Yan, J., Forbus, K.D., & Gentner, D. (2003). A theory of rerepresentation in analogical matching. Proc. 25th Annual Meeting of the Cognitive Science Society, pp. 1265–1270. Mahwah, NJ: Erlbaum.Google Scholar