A Bayesian support tool for morphological design

Abstract

Dynamic computer based support tools for the conceptual design phase have provided a long-standing challenge to develop. This is largely due to the 'fluid' nature of the conceptual design phase. Design evaluation methods, which form the basis of most computer design support tools, provide poor support for multiple outcomes. 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.