Machine Learning Stochastic Design Models

Quentin Falcone
Peyteon High School

Abstract

Due to the fluid nature of the early stages of the design process,
it is difficult to obtain deterministic product design evaluations.
This is primarily due to the flexibility of the design at this stage,
namely that there can be multiple interpretations of a single design
concept. However, it is important for designers to understand how
these design concepts are likely to fulfil the original specification,
thus enabling the designer to select or bias towards solutions with
favourable outcomes. One approach is to create a stochastic model
of the design domain. This paper tackles the issues of using a
product database to induce a Bayesian model that represents the
relationships between the design parameters and characteristics.
A greedy learning algorithm is presented and illustrated using a
simple case study.

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