In this project we advance a research plan to develop methods that will enable a decision-maker to make interactive, real-time trade-off investigations for multi-attribute design problems, under certainty or uncertainty. These trade-off investigations will focus on identifying which design candidates best satisfy the performance objectives (as represented by the multiple attributes), which design candidates best satisfy some subset of performance objectives, and which design candidates remain 'good' candidates, even under uncertainty related to incompleteness of knowledge or random factors in the original design problem. The Visual Hyperspace Pareto Frontier (VHPF) will be developed to enable real-time decision-making, to address the complexity involved in any large multiattribute problem, and to accommodate the incorporation of uncertainty considerations. The long-term goals of the project are 1) to support decision-making in design by providing a visualization-based tool to meaningful explore multidimensional performance space in multi-attribute design, 2) to provide a lossless dimension blending technique, and 3) to integrate the blending technique with multidimensional visualization. The broader impacts are expected from the capability how data can be synthesized and processed appropriately so as to derive use, particularly when coming from many different disciplines, such as for structure determination for genomics, or entertainment simulation, or engineering analysis of complex systems.
