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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 ,
John Gero  and
Rob Saunders
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: [email protected]
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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.

Keywords

Computational Analogy MakingInterpretationRepresentation
Type
Special Issue Articles
Information
AI EDAM , Volume 29 , Issue 2: Analogical Thinking , May 2015 , pp. 185 - 201
Copyright
Copyright © Cambridge University Press 2015 

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