example

An action inferring facility for a multimodal interface called Edward is described. Based on the actions the user performs, Edward anticipates future actions and offers to perform them automatically. The system uses inductive inference to anticipate actions. It generalizes over arguments and results, and detects patterns on the basis of a small sequence of user actions, e.g. “copy a lisp file; change extension of original file into .org; put the copy in the backup folder”. Multimodality (particularly the combination of natural language and simulated pointing gestures) and the reuse of patterns are important new features. Some possibilities and problems of action inferring interfaces in general are addressed. Action inferring interfaces are particularly useful for professional users of general-purpose applications. Such users are unable to program repetitive patterns because either the applications do not provide the facilities or the users lack the capabilities.

Many tasks performed using computer interfaces are very repetitive. While programmers can write macros or procedures to automate these repetitive tasks, this requires special skills. Demonstrational systems make macro building accessible to all users, but most provide either no visual representation of the macro or only a textual representation. We have developed a history-based visual representation of commands in a graphical user interface. This representation supports the definition of macros by example in several novel ways. At any time, a user can open a history window, review the commands executed in a session, select operations to encapsulate into a macro, and choose objects and their attributes as arguments. The system has facilities to generalize the macro automatically, save it for future use, and edit it.

Graphical user interfaces (GUI) provide intuitive and easy means for users to communicate with computers. However, construction of GUI software requires complex programming that is far from being intuitive. Because of the “semantic gap” between the textual application program and its graphical interface, the programmer himself must conceptually maintain the correspondence between the textual programming and the graphical image of the resulting interface. Instead, we propose a programming environment based on the programming by visual example (PBVE) scheme, which allows the GUI designers to “program” visual interfaces for their applications by “drawing” the example visualization of application data with a direct manipulation interface. Our system, TRIP3, realizes this with (1) the bi-directional translation model between the (abstract) application data and the pictorial data of the GUI, and (2) the ability to generate mapping rules for the translation from example application data and its corresponding example visualization. The latter is made possible by the use of generalization of visual examples, where the system is able to automatically generate generalized mapping rules from a given set of examples.

The construction of application-specific Graphical User Interfaces (GUI) still needs considerable programming partly because the mapping between application data and its visual representation is complicated. This study proposes a system which generates GUIs by generalizing multiple sets of application data and its visualization examples. The most notable characteristic of the system is that programmers can interactively modify the mapping by “correcting” the system-generated visualization examples that represent the system's current notion of programmer's intentions. Conflicting mappings are automatically resolved via the use of constraint hierarchies.

We propose a new evolutionary method of extracting user preferences from examples shown to an automatic graph layout system. Using stochastic methods such as simulated annealing and genetic algorithms, automatic layout systems can find a good layout using an evaluation function which can calculate how good a given layout is. However, the evaluation function is usually not known beforehand, and it might vary from user to user. In our system, users show the system several pairs of good and bad layout examples, and the system infers the evaluation function from the examples using genetic programming technique. After the evaluation function evolves to reflect the preferences of the user, it is used as a general evaluation function for laying out graphs. The same technique can be used for a wide range of adaptive user interface systems.

Source-code examples of APIs enable developers to quickly gain a gestalt understanding of a library's functionality, and they support organically creating applications by incrementally modifying a functional starting point. As an increasing number of web sites provide APIs, significantlatent value lies in connecting the complementary representations between site and service - in essence, enabling sites themselves to be the example corpus. We introduce d.mix, a tool for creating web mashups that leverages this site-to-service correspondence. With d.mix, users browse annotated web sites and select elements to sample. d.mix's sampling mechanism generates the underlying service calls that yield those elements. This code can be edited, executed, and shared in d.mix's wiki-based hosting environment. This sampling approach leverages pre-existing web sites as example sets and supports fluid composition and modification of examples. An initial study with eight participants found d.mix to enable rapid experimentation, and suggested avenues for improving its annotation mechanism.