Eliciting user-task models is a thorny problem in modelbased user interface design, and communicating domainspecific knowledge from an expert to a knowledge engineer is a continuing problem in knowledge acquisition.We devised a task elicitation method that capitalizes on a domain expert's ability to describe a task in plain English, and on a knowledge engineer's skills to formalize it.The method bridges the gap between the two by helping the expert refine the description and by giving the engineer clues to its structure.We implemented and evaluated an interactive tool called the User-Task Elicitation Tool (U-TEL) to elicit user-task models from domain experts based on our methodology.Via direct manipulation, U-TEL provides capabilities for word processing, keyword classification, and outline refinement, By using U-TEL, domain experts can refine a textual specification of a user task into a basic user-task model suitable for use in model-based interface development environments.Our evaluation shows that U-TEL can be used effectively by domain experts with or without a background in programming or interface modeling, and that the tool can be a key element in promoting user-centered interface design in model-based systems.

This paper describes ActionStreams, a system for inducing task models from observations of user activity.The model can represent several task structures: hierarchy, variable sequencing, mandatory vs. optional actions, and interleaved sequences.The task models can be used for just-in-time automation and for guidance in user interface design.

Article Free Access Share on Management of interface design knowledge with MOBI-D Authors: Angel R. Puerta Knowledge Systems Laboratory, Stanford University, MSOB x215, Stanford, CA Knowledge Systems Laboratory, Stanford University, MSOB x215, Stanford, CAView Profile , David Maulsby Knowledge Systems Laboratory, Stanford University, MSOB x215, Stanford, CA Knowledge Systems Laboratory, Stanford University, MSOB x215, Stanford, CAView Profile Authors Info & Claims IUI '97: Proceedings of the 2nd international conference on Intelligent user interfacesJanuary 1997 Pages 249–252https://doi.org/10.1145/238218.238337Published:06 January 1997Publication History 18citation353DownloadsMetricsTotal Citations18Total Downloads353Last 12 Months15Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

The paper describes a technique that constructs models of symbol sequences in the form of small, human-readable, hierarchical grammars. The grammars are both semantically plausible and compact. The technique can induce structure from a variety of different kinds of sequence, and examples are given of models derived from English text, C source code and a sequence of terminal control codes. It explains the grammatical induction technique, demonstrates its application to three very different sequences, evaluates its compression performance, and concludes by briefly discussing its use as a method for knowledge acquisition.>

Programming by example is a powerful way of bestowing on nonprogrammers the ability to communicate tasks to a computer. When creating procedures from examples it is necessary to be able to infer the existence of variables, conditional branches, and loops. This article explores the role of empirical or “similarity-based” learning in this process. For a concrete example of a procedure induction system, we use an existing scheme called METAMOUSE which allows graphical procedures to be specified from examples of their execution. A procedure is induced from the first example, and can be generalized in accordance with examples encountered later on. We describe how the system can be enhanced with Mitchell's candidate elimination algorithm, one of the simplest empirical learning techniques, to improve its ability to recognize constraints in a comprehensive and flexible manner. Procedure induction is, no doubt, a very complex task. This work revealed usefulness and effectiveness of empirical learning in procedure induction, although it cannot be a complete substitute for specific preprogrammed, domain knowledge in situations where this is readily available. However, in domains such as graphical editing, where knowledge is incomplete and/or incorrect, the best way to pursue may prove to be a combination of similarity- and explanation-based learning. © 1994 John Wiley & Sons, Inc.

Moctec is a set of interactive mockups of an interface for programming search and replace tasks by example. The user guides inference by pointing at relevant features of data.

Metarnouse is a demonstrational interface for graphical editing tasks within a drawing program. The user specifies a procedure by performing an example execution trace and creating graphical tools where necessary to help make constraints explicit. The system generalizes the user's action sequence, identifying key features of individual steps and disregarding coincidental events. It creates a program with loops and conditional branches as appropriate and predicts upcoming actions, thereby reducing the tedium of repetitive and precise graphical editing. It uses default reasoning about graphical constraints to make initial generalizations and enables the user to correct these hypotheses either by rejecting its predictions or by editing iconic descriptors it displays after each action.

Article Demonstrational interfaces: Coming soon? Share on Authors: Brad A. Myers School of Computer Science, Carnegie Mellon University, Pittsburgh, PA School of Computer Science, Carnegie Mellon University, Pittsburgh, PAView Profile , Allen Cypher Apple Computer, Inc. Apple Computer, Inc.View Profile , David Maulsby University of Calgary University of CalgaryView Profile , David C. Smith Apple Computer, Inc. Apple Computer, Inc.View Profile , Ben Shneiderman University of Maryland University of MarylandView Profile Authors Info & Claims CHI '91: Proceedings of the SIGCHI Conference on Human Factors in Computing SystemsApril 1991 Pages 393–396https://doi.org/10.1145/108844.108966Published:01 March 1991 10citation335DownloadsMetricsTotal Citations10Total Downloads335Last 12 Months4Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

End users who need to program within highly interactive direct-manipulation interfaces should be able to communicate their intentions through concrete demonstration rather than in terms of symbolic abstraction. This paper describes a system that learns procedures in interactive graphics taught to it “by example” by minimally trained users. It shows how techniques of machine learning and reactive interfaces can support one another—the former providing generalization heuristics to identify constraints implicit in user actions, the latter offering immediate feedback to help the user clarify hidden constraints and correct errors before they are planted into the procedure. The teacher's attention is focused on the learning system's perceptual and inferential shortcomings through a metaphorical apprentice called Metamouse, which generalizes action sequences on the fly and eagerly carries out any actions it can predict. The success of the induction process is assessed quantitatively by counting erroneous predictions made during example tasks.

Metamouse is a device enabling the user of a drawing program to specify graphical procedures by supplying example execution traces. The user manipulates objects directly on the screen, creating graphical tools where necessary to help make constraints explicit; the system records the sequence of actions and induces a procedure. Generalization is used both to identify the key features of individual program steps, disregarding coincidental events; and to connect the steps into a program graph, creating loops and conditional branches as appropriate. Metamouse operates within a 2D click-and-drag drafting package, and incorporates a strong model of the relative importance of different types of graphical constraint. Close attention is paid to user interface aspects, and Metamouse helps the user by predicting and performing actions, thus reducing the tedium of repetitive graphical editing tasks.