Abstract
A method for flexibly searching the conceptual design space using a stochastic approach is presented. From a database of previous design exemplars, a novel and inexpensive algorithm is used to induce a Bayesian Belief Network (BBN) that represents the causal relationships between a design domain’s variables. This BBN is then used as part of an interactive tool for stochastically searching the conceptual design space using two search heuristics. This method is illustrated using a number of design scenarios based on a conceptual car design domain. The paper concludes with future research avenues to further the functionality of the BBN-based design search tool.
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MATTHEWS, P. (2006). BAYESIAN NETWORKS FOR DESIGN. In: GERO, J.S. (eds) Design Computing and Cognition ’06. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-5131-9_12
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DOI: https://doi.org/10.1007/978-1-4020-5131-9_12
Publisher Name: Springer, Dordrecht
Print ISBN: 978-1-4020-5130-2
Online ISBN: 978-1-4020-5131-9
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