Multiobjective intelligent computer-aided design

A methodology for multiobjective intelligent computer-aided design (MICAD) of large-scale systems is proposed. The proposed MICAD methodology partitions the knowledge base required for the design system to include a progressively acquired preferential component captured using a decision theoretic model and an a priori acquired operational component that can be represented using a wide range of artificial intelligence and expert systems techniques. User preferences are employed to personalize the search for improved designs and dynamic acquisition of those preferences enables the systems, to some extent, to mimic and conform to different users, decision-making styles, and design goals. The primary emphasis is the decision theoretic treatment of user preference in a general search strategy and design domain. The construction of the operational knowledge base is demonstrated to be an opportunity for application of existing artificial intelligence techniques