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Using Symbol Emergence to Discover Multi-Lingual Translations in Design

[+] Author Affiliations
Amitabha Mukerjee, Madan Mohan Dabbeeru

Indian Institute of Technology Kanpur, Kanpur, UP, India

Paper No. DETC2010-29216, pp. 317-327; 11 pages
doi:10.1115/DETC2010-29216
From:
  • ASME 2010 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 5: 22nd International Conference on Design Theory and Methodology; Special Conference on Mechanical Vibration and Noise
  • Montreal, Quebec, Canada, August 15–18, 2010
  • Conference Sponsors: Design Engineering Division and Computers in Engineering Division
  • ISBN: 978-0-7918-4413-7 | eISBN: 978-0-7918-3881-5
  • Copyright © 2010 by ASME

abstract

Incorporating design knowledge into computational design requires “symbols” — but this term as used in knowledge-based models of design is a formal term, defined only in terms of other symbols. For most humans, symbols are [term : meaning] pairs that emerge while interacting with real designs. However, both the term and its interpretation vary considerably across design groups, particularly in today’s international cooperative design scenario. For translating symbols in design, one needs to incorporate the design context, which is since the actual design object and its characteristics form the most relevant part of the context. In this work, we consider an embodied symbols approach towards translation, where models corresponding to symbol semantics are discovered based on functional norms in a given design context. The functions are available as performance measures on a given task, and lead to low-dimensional characterizations (called image schema ) that reveal inter-relations in the input space that must hold for functional validity. Some of these image schemas eventually acquire language labels and become symbols. Since different designers differ in experience and in language their symbols differ somewhat. Here we consider how independent language agents may map these low-dimensional characterizations (called chunks) to units of languages based on human commentary produced in the same context. We demonstrate how this process may work for the simple domain of insertion tasks and fits, and learn both the image schemas and the language labels in two different languages, English and Telugu.

Copyright © 2010 by ASME
Topics: Design

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