A Bayesian learning of probabilistic relations between perceptual attributes and technical characteristics of car dashboards to construct a perceptual evaluation model

Walid Ben Ahmed and Bernard Yannou

International Journal of Product Development, 2009, vol. 7, issue 1/2, 47-72

Abstract: Starting from a primary perceptual evaluation of a set of car dashboards, we propose to build a Bayesian network (BN) between perceptual attributes and design attributes. Two types of learning processes may be considered: supervised BN when the prediction on a targeted attribute must be optimised and unsupervised BN otherwise. These two types of BNs are considered along three design simulation scenarios: the direct scenario which consists of the prediction of a design change impact on customer perceptions, the inverse scenario for fixing design characteristics so as to result in an expected customer perception, and a more realistic combined scenario.

Keywords: decision making; Kansei engineering; design synthesis; perceptual evaluation; emotional design; Bayesian networks; emotion; automotive dashboards; dashboard design; vehicle design; design attributes; design change; customer perceptions. (search for similar items in EconPapers)
Date: 2009
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