Automated support of design teams, consisting of both human and automated systems, requires an understanding of the role of trust in distributed design processes. By explicating trust, an individual designer's decisions become better understood and may be better supported. Each individual designer has his or her private goals in a cooperative design setting, in which requirement conflicts and resource competitions abound. However, there are group goals that also need to be reached. This paper presents an overview of research related to trust in the context of agents and design, a computational knowledge-level model of trust based on the seven beliefs distinguished by Castelfranchi and Falcone, and an example of the use of the trust model in a specific design process, namely, Website design from the perspective of a single designer. The results are discussed in the context of distributed design in open systems.

Mass customization has been identified as a competitive strategy by an increasing number of companies. Family-based product design is an efficient and effective means to realize sufficient product variety, while satisfying a range of customer demands in support for mass customization. This paper presents a knowledge decision support approach to product family design evaluation and selection for mass customization process. Here, product family design is viewed as a selection problem with the following stages: product family (design alternatives) generation, product family design evaluation, and selection for customization. The fundamental issues underlying product family design for mass customization are discussed. Then, a knowledge support framework and its relevant technologies are developed for module-based product family design for mass customization. A systematic fuzzy clustering and ranking model is proposed and discussed in detail. This model supports the imprecision inherent in decision making with fuzzy customers' preference relations and uses fuzzy analysis techniques for evaluation and selection. A neural network technique is also adopted to adjust the membership function to enhance the model. The focus of this paper is on the development of a knowledge-intensive support scheme and a comprehensive systematic fuzzy clustering and ranking methodology for product family design evaluation and selection. A case study and the scenario of knowledge support for power supply family evaluation, selection, and customization are provided for illustration.