The goal of the research described in this paper is to develop an application-independent presentation tool that automatically designs effective graphical presentations (such as bar charts, scatter plots, and connected graphs) of relational information. Two problems are raised by this goal: The codifi-cation of graphic design criteria in a form that can be used by the presentation tool, and the generation of a wide variety of designs so that the presentation tool can accommodate a wide variety of information. The approach described in this paper is based on the view that graphical presentations are sentences of graphical languages. The graphic design issues are codified as expressiveness and effectiveness criteria for graphical languages. Expressiveness criteria determine whether a graphical language can express the desired information. Effectiveness criteria determine whether a graphical language exploits the capabilities of the output medium and the human visual system. A wide variety of designs can be systematically generated by using a composition algebra that composes a small set of primitive graphical languages. Artificial intelligence techniques are used to implement a prototype presentation tool called APT (A Presentation Tool), which is based on the composition algebra and the graphic design criteria.

A new method for designing multivariate data visualization tools is presented. Multivariate data visualization involves representation of data elements with multiple dimensions in a low dimensional enviroment, such as a computer screen or printed media. Our tools are designed to allow users to perform simple tasks like estimation, target detection, and detection of data boundaries rapidly and accurately. These techniques could be used for large datasets where more traditional techniques do not work well or for time-sensitive applications that require rapid understanding and informative data displays. Our design technique is based on principles arising in an area of cognitive psychology called preattentive processing. Preattentive processing studies visual features that are \preatten-tively " detected by the human visual system. Viewers do not have to focus their attention on particular regions of an image to determine whether elements with certain features are present or absent. Examples of preattentive features include colour, orientation, intensity, size, shape, curvature, and line length. Because this ability is part of the low-level human visual system, detection is performed very rapidly, almost certainly using a large degree of parallelism. In

This dissertation explores the concepts behind creating useful visualizations for the analysis and understanding of simulation environments. We define parameters for determining usefulness of visualizations, describe a methodology of visualization creation, and use this methodology to create a computerized automatic visualization generation system for an urban planning simulation system. The effective use of visualizations can lessen the burden of data retrieval and allows analysts to focus on information synthesis. Our visualization system allows users to specify a goal or task, and then the system designs visual displays that help users perform their tasks and achieve their goals. Our aim is to allow users to focus on their tasks rather than worry about the specifics of effective information presentation.