概念设计中机器学习技术的运用

设计中开发计算理论的一个挑战是必须能支持计算机的有效运用，这一机制允许从设计专家那儿或设计样例中取得产生，累加和转换的设计知识。而其中的一个方法是把机器学习机制综合成基于知识的支持系统，以模拟设计过程初级阶段，使设计成为一个增加和诱导学习的过程。模拟的需要产生于在不同的提取阶段获取，提炼和转移设计知识的需求，从而使得能轻而易举的熟练操作。在设计中，现有的知识产生于过去的设计解决方案，而过去的解决方案提供的反馈信息能更新和提高设计理论知识基础。但是，没有学习接受能力，设计系统不能反映设计家们在这一领域的成长经历，也不能反映设计家们从以往设计案例中提取知识的能力。在此提出了方案设计和效力评价中的三种方法。

Application of machine learning techniques in conceptual design

One of the challenges of developing a computational theory of the design process is to support learning by the use of computational mechanisms that allow for the generation, accumulation and transformation of design knowledge learned from design experts, or design examples. One of the ways in which machine learning techniques can be integrated in a knowledge based design support system is to model the early stage of the design process as an incremental and inductive learning of design problem structures. The need for such a model arises from the need of capturing, refining, and transferring design knowledge at different levels of abstraction so that it can be manipulated easily. In design, the knowledge generated from past design solutions provides a feedback for modifying and enhancing the design knowledge base. Without a learning capability a design system is unable to reflect the designer's growing experience in the field and designer's ability to use knowledge abstracted from past design cases. In this paper, we present three applications of machine learning techniques in conceptual design and evaluate their effectiveness.