Multiobjective intelligent computer-aided design

Abstract

A methodology is proposed for multiobjective intelligent computer-aided design (MICAD) of large scale systems in problem domains characterized by: highly dimensional search spaces not effectively reducible by problem decomposition; inexplicit, nondifferentiable, and nonmonotonic cost and performance functions; and a highly preferential design context. 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 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 system, to some extent, to mimic and conform to different users, decisionmaking styles, and design goals. The primary emphasis of the paper 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. It is argued that system capability and user-acceptability are enhanced by this integrated OR/AI paradigm.