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Analogical recognition of shape and structure in design drawings

Published online by Cambridge University Press:  14 March 2008

Patrick W. Yaner  and
Ashok K. Goel
Patrick W. Yaner
Affiliation:
LogicBlox, Inc., Atlanta, Georgia, USA
Ashok K. Goel
Affiliation:
Design Intelligence Laboratory, School of Interactive Computing, Georgia Institute of Technology, Atlanta, Georgia, USA
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Abstract

We describe a method for constructing a structural model of an unlabeled target two-dimensional line drawing by analogy to a known source model of a drawing with similar structure. The source case is represented as a schema that contains its line drawing and its structural model represented at multiple levels of abstraction: the lines and intersections in the drawing, the shapes, the structural components, and connections of the device are depicted in the drawing. Given a target drawing and a relevant source case, our method of compositional analogy first constructs a representation of the lines and the intersections in the target drawing, then uses the mappings at the level of line intersections to transfer the shape representations from the source case to the target; next, it uses the mappings at the level of shapes to transfer the full structural model of the depicted system from the source to the target.

Keywords

Analogical ReasoningCase-Based ReasoningDesignDiagrammatic ReasoningDrawingsVisual Reasoning
Type
Research Article
Information
AI EDAM , Volume 22 , Issue 2 , Spring 2008 , pp. 117 - 128
Copyright
Copyright © Cambridge University Press 2008

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References

REFERENCES

Alvarado, C., & Davis, R. (2005). Dynamically constructed bayes nets for multi-domain sketch recognition. Proc. 19th Int. Joint Conf. Artificial Intelligence (IJCAI-05), pp. 1407–1412. San Mateo, CA: Morgan Kaufmann.Google Scholar
Bitner, J.R., & Reingold, E.M. (1975). Backtrack programming techniques. Communications of the ACM 18 (11), 651–656.CrossRefGoogle Scholar
Börner, K. (2001). Efficient case-based structure generation for design support. Artificial Intelligence Review 16 (2), 87–118.CrossRefGoogle Scholar
Cardone, A., Gupta, S.M., & Karnik, M. (2003). A survey of shape similarity assessment algorithms for product design and manufacturing applications. Journal of Computing and Information Science in Engineering 3 (2), 109–118.CrossRefGoogle Scholar
Falkenhainer, B., Forbus, K.D., & Gentner, D. (1990). The structure-mapping engine: algorithms and examples. Artificial Intelligence 41 (1), 1–63.CrossRefGoogle Scholar
Ferguson, E.S. (1992). Engineering and the Mind's Eye. Cambridge, MA: MIT Press.Google Scholar
Ferguson, R.W., & Forbus, K.D. (2000). GeoRep: a flexible tool for spatial representation of line drawings. Proc. 17th National Conf. Artificial Intelligence (AAAI-2000) Menlo Park, CA: AAAI Press.Google Scholar
Goel, A.K. (1991). Model revision: A theory of incremental model learning. Proc. 8th Int. Conf. Machine Learning (ICML-91), pp. 605–609. San Mateo, CA: Morgan Kaufmann.CrossRefGoogle Scholar
Goel, A.K. (1996). Adaptive modeling. Proc. 10th Int. Workshop on Qualitative Reasoning. Stanford Sierra Camp, Stanford, CA.Google Scholar
Goel, A.K., & Chandrasekaran, B. (1989). Functional representation in design and redesign problem solving. Proc. 11th Int. Joint Conf. Artificial Intelligence (IJCAI-89), pp. 1388–1394. San Mateo, CA: Morgan Kaufmann.Google Scholar
Gross, M.D., & Do, E. (2000). Drawing on the back of an envelope: a framework for interacting with application programs by freehand drawing. Computers & Graphics 24, 835–849.CrossRefGoogle Scholar
Holyoak, K.J., & Thagard, P. (1989). Analogical mapping by constraint satisfaction. Cognitive Science 13 (3), 295–355.CrossRefGoogle Scholar
Iyer, N., Jayanti, S., Lou, K., Kalyanaraman, Y., & Ramani, K. (2005). Three-dimensional shape searching: state-of-the-art review and future trends. Computer-Aided Design 37 (5), 509–530.CrossRefGoogle Scholar
Jupp, J., & Gero, J.S. (2004). Qualitative representation and reasoning about shapes and spatial relationships. In Visual and Spatial Reasoning in Design III (Gero, J.S., Tversky, B., & Knight, T., Eds.), pp. 139–162. Sydney: University of Sydney, Key Centre of Design Computing and Cognition.Google Scholar
Kondrak, G., & Van Beek, P. (1997). A theoretical evaluation of selected backtracking algorithms. Artificial Intelligence 24 (1–2), 365–387.CrossRefGoogle Scholar
Klenk, M., Forbus, K.D., Tomai, E., Kim, H., & Kyckelhahn, B. (2005). Solving everyday physical reasoning problems by analogy using sketches. Proc. 20th National Conf. Artificial Intelligence (AAAI-05), pp. 209–215. Menlo Park, CA: AAAI Press.Google Scholar
Kramer, G. (1993). A geometric constraint engine. Artificial Intelligence 58 (1–3), 327–360.CrossRefGoogle Scholar
Larkin, J., & Simon, H. (1987). Why a diagram is (sometimes) worth a thousand words. Cognitive Science 11 (1), 65–99.CrossRefGoogle Scholar
Ullman, D.G., Wood, S., & Craig, D. (1990). The importance of drawing in the mechanical design process. Computer Graphics 14 (2), 263–274.CrossRefGoogle Scholar
Yaner, P.W., & Goel, A.K. (2006). Visual analogy: viewing analogical retrieval and mapping as constraint satisfaction problems. Applied Intelligence 25 (1), 91–105.CrossRefGoogle Scholar
Yaner, P.W., & Goel, A.K. (2007). Understanding drawings by compositional analogy. Proc. 20th Int. Joint Conf. Artificial Intelligence (IJCAI-07), pp. 1131–1137. San Mateo, CA: Morgan Kaufmann.Google Scholar