This article shows how the concept of affordance in the user interface fits into a wellunderstood artificial intelligence (AI) model of acting in an environment. In this model AI planning research is used to interpret affordances in terms of the costs associated with the generation and execution of operators in a plan. We motivate our approach with a brief survey of the affordance literature and its connections to the planning literature, and then explore its implications through examples of common user interface mechanisms described in affordance terms. Despite its simplicity, our modeling approach ties together several different threads of practical and theoretical work on affordance into a single conceptual framework. Affordances in a planning representation 3 Contents 1 INTRODUCTION 4 2 PERSPECTIVES ON THE NATURE OF AFFORDANCES 5 3 AFFORDANCES IN PLANNING TERMS 8 4 GENERIC USER INTERFACE AFFORDANCES 13 4.1 Programmable User Models for Affordance Evaluation . . . . . . . . . . ....

Knowledge discovery in databases (KDD) is an increasingly widespread activity. KDD processes may entail the use of a large number of data manipulation and analysis techniques, and new techniques are being developed on an ongoing basis. A challenge for the effective use of KDD is coordinating the use of these techniques, which may be highly specialized, conditional and contingent. Additionally, the understanding and validity of KDD results can depend critically on the processes by which they were derived. We propose to use process programming to address the coordination of agents in the use of KDD techniques. We illustrate this approach using the process language Little-JIL to program a representative bivariate regression process. With Little-JIL programs we can clearly capture the coordination of KDD activities, including control flow, pre- and post-requisites, exception handling, and resource usage.

Computational workflows are a powerful paradigm to represent and manage complex applications, particularly in large-scale distributed scientific data analysis. Workflows represent application components that result in individual computations as well as their interdependencies in terms of data flow. Workflow systems use these representations to manage various aspects of workflow creation and execution for users, such as the automatic assignment of execution resources. This paper describes an approach to automating a new aspect of the process: the selection of application components and data sources. We present a novel approach that enables users to specify varying degrees of detail and amount of constraints in a workflow request, including the specification of constraints on input, intermediate, or output data in the workflow, abstract workflow component classes rather than specific component implementations, and generic reusable workflow templates that express a pre-defined combination of components. The algorithm elaborates the user request into a set of fully ground workflows with specific choices of data sources and codes to be used so that they can

This paper describes an automated visualization assistant called ViA. ViA is designed to help users construct perceptually optimal visualizations to represent, explore, and analyze large, complex, multidimensional datasets. We have approached this problem by studying what is known about the control of human visual attention. By harnessing the low-level human visual system, we can support our dual goals of rapid and accurate visualization. Perceptual guidelines that we have built using psychophysical experiments form the basis for ViA. ViA uses modified mixed-initiative planning algorithms from artificial intelligence to search for perceptually optimal data attribute to visual feature (data-feature) mappings. Our perceptual guidelines are integrated into evaluation engines that provide evaluation weights for a given data-feature mapping, and hints on how that mapping might be improved. ViA begins by asking users a set of simple questions about their dataset and the analysis tasks they w...

. Interactive planning systems are often built with sophisticated graphical user interfaces that display agents in a physical and spatial simulation. While this gives the user concrete details that make it easier to gain a strategic perspective on planning problems, it may make some forms of abstraction more difficult. This issue can be addressed by IDA. Some IDA techniques generate descriptions and explanations of patterns in data via abstraction and data reduction. These techniques are well-suited to the needs of an planning simulation, where the amount of potentially relevant information can be overwhelming for a user. This paper describes an extension of a planning simulation by IDA techniques; the resulting integration demonstrates some of the complementary properties of these two areas of research. 1

We propose and evaluate an agenda- and justification-based architecture for discovery systems that selects the next tasks to perform, as well as heuristics for use in discovery systems. This framework has many desirable properties: (1) it selects its own tasks to perform based upon how plausible they are judged to be; (2) it facilitates the encoding of general discovery strategies using a variety of background knowledge; and (3) it tailors its behavior toward a user’s interests. Many experiments with a prototype discovery program called HAMB demonstrate that both reasons and estimates of interestingness contribute to performance in the domains of protein crystallization and patient rehabilitation data. The program’s heuristics provide good initial solutions to problems encountered when implementing fully autonomous discovery systems.

Making an interface more efficient, in a task analysis sense, can make it more difficult for an automated reasoning system to infer user goals, by eliminating some user actions, by presenting information without requiring overt user selection, and so forth. We have built an assistant for exploratory statistical analysis that imposes structure on user interaction such that the efficiency for users is somewhat reduced, but the increased level of detail in their actions leads to easier interpretation by the system. We discuss the issues that led to our design, which involves navigation-based interaction and a data mountain for organizing results. A formative user evaluation shows the potential benefits of the approach.

A data mining (DM) process involves multiple stages. A simple, but typical, process might include preprocessing data, applying a data-mining algorithm, and postprocessing the mining results. There are many possible choices for each stage, and only some combinations are valid. Because of the large space and non-trivial interactions, both novices and data-mining specialists need assistance in composing and selecting DM processes. We present the concept of Intelligent Discovery Assistants (IDAs), which provide users with (i) systematic enumerations of valid DM processes, in order that important, potentially fruitful options are not overlooked, and (ii) effective rankings of these valid processes by different criteria, to facilitate the choice of DM processes to execute. We use a prototype to show that an IDA can indeed provide useful enumerations and effective rankings. We discuss how an IDA is an important tool for knowledge sharing among a team of data miners. Finally, we illustrate all the claims with a comprehensive demonstration using a more involved process and data from the 1998 KDDCUP competition.

Abstract not found

A data mining (DM) process involves multiple stages. A simple, but typical, process might include preprocessing data, applying a data-mining algorithm, and postprocessing the mining results. There are many possible choices for each stage, and only some combinations are valid. Because of the large space and non-trivial interactions, both novices and data-mining specialists need assistance in composing and selecting DM processes. We present the concept of Intelligent Discovery Assistants (IDAs), which provide users with (i) systematic enumerations of valid DM processes, in order that important, potentially fruitful options are not overlooked, and (ii) effective rankings of these valid processes by different criteria, to facilitate the choice of DM processes to execute. We use a prototype to show that an IDA can indeed provide useful enumerations and effective rankings. We discuss how an IDA is an important tool for knowledge sharing among a team of data miners. Finally, we illustrate all the claims with a comprehensive demonstration using a more involved process and data from the 1998 KDDCUP competition.