A Bayesian support tool for morphological design

Abstract

Publisher's copyright statement:Additional information: Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. This research proposes a stochastic-based support tool that addresses this problem.A Bayesian Belief Network (BBN) is used to represent the causal links between design variables. Included in this research is an efficient method for learning a design domain network from previous design data in the structure of a morphological design chart. This induction algorithm is based on information content. A user-interface is proposed to support dynamically searching the conceptual design space, based on a partial design specification. This support tool is empirically compared against a more traditional search process. While no compelling evidence is produced to support the stochastic based approach, an interesting broader design search behaviour emerges from observations of the use of the stochastic design support tool.

Challenges to Bayesian decision support using morphological matrices for design: empirical evidence

“…This observation was primarily to evaluate the effectiveness of the user interface. The full results are reported in Matthews (2008). The key points of this trail are summarised here, with a focus on the benefits and challenges that were observed.…”

“…The stochastic representation enables the opposite outcome in both events, however these are represented by accordingly low probabilities. Further details of this illustration are provided by Matthews (2008).…”

“…The scope of this paper presents the qualitative aspects of this study that inform the specific challenges of deploying a stochastic design decision support tool. An in-depth quantitative analysis of this study alone is presented in Matthews (2008).…”

“…Hence, for realistic design domains, this is not an appealing method. A computationally more efficient approach has been adopted based on the information content that would be contained in any potential causal arc (Matthews, 2006(Matthews, , 2008 Figure 4: The design process using the BBN starts with the design specification being entered into the BBN (filled nodes). The conceptual design is then iteratively completed by the designer, guided by the variable PDFs.…”

“…Where the causal ordering of the variables is not known in advance, they demonstrate that it is possible to learn a suitably accurate network complexity of O(n 4 ) in terms of the number of variables. The approach described in Matthews (2008) uses a greedy algorithm that learns a network with complexity O(n 2 ). This approach uses the basic definition of conditional probability:…”