We present Inference Bear (Inference Based On Before And After Snapshots) which lets users build functional graphical user interfaces by demonstration. Inference Bear is the first Programming By Demonstration system based on the abstract inference engine described in [5]. Among other things, Inference Bear lets you align, center, move, resize, create and delete user interface elements by demonstration. Its most notable feature is that it does not use domain knowledge in its inferencing. INTRODUCTION We use a technique for demonstrating behavior which is inspired by the "before" and "after" pictures often found in advertisements. Behavior is demonstrated by supplying a Before picture, an After picture and an event that causes the transition from the Before to the After picture. A single example is sufficient for simple behavior, more examples are required to infer more complex behavior. Snapshots have been used in earlier demonstrational systems, such as [7], which lets the user take ...

Many different methods have been used to specify user interfaces: algebraic specification, grammars, task description languages, transition diagrams with and without extensions, rule-based systems, and by demonstration. However, none of these methods has been widely adopted. Current user interfaces are still built by writing a program, perhaps with the aid of a UIMS. There are two principal reasons for this. First, specification languages are difficult to use. Reading a specification and understanding its exact meaning is very difficult. Writing a correct specification is even more difficult. Second, most specification languages are not executable. This means that after the user interface programmer makes the effort to write a specification, the user interface must still be coded. As a consequence, most programmers have little incentive to do a specification. A pilot study into the comprehensibility of specifications is described. The results of this study suggest that user interface s...

This report proposes a methodology for selecting appropriate interaction techniques and devices which meet the motor, sensory, communication and cognitive abilities of different user groups. The proposed solutions are targeted to B-ISDN services and assume the multimedia capabilities of a broadband network environment. The methodology mainly concerns the User Interface (UI) part which handles the low level (i.e lexical) interaction issues - i.e. those related to the external representation of service information and to the UI properties which are directly viewed/manipulated by the user. It incorporates a theoretical investigation of lexical interaction issues together with the results of a practical evaluation of some of the identified lexical interaction possibilities. The theoretical work identifies some important interaction tasks and their associated techniques/devices, as a basic set of design elements on which lexical interaction in a multimedia/multimodal environment can be buil...

This paper consists of two parts. The first section makes a case for spending some time at the symposium designing a common representation for user tasks and their automation. The second section briefly presents Grizzly Bear, a demonstrational tool that I have built over the last three years (and which taught me the importance of a well-defined underlying representation the hard way).

Many graphics tasks, such as the manipulation of graphical objects, and the construction of userinterface widgets, can be facilitated by geometric constraints. However, the difficulty of specifying constraints by traditional methods forms a barrier to their widespread use. In order to make constraints easier to declare, we have developed a method of specifying constraints implicitly, through multiple examples. Snapshots are taken of an initial scene configuration, and one or more additional snapshots are taken after the scene has been edited into other valid configurations. The constraints that are satisfied in all the snapshots are then applied to the scene objects. We discuss an efficient algorithm for inferring constraints from multiple snapshots. The algorithm has been incorporated into the Chimera editor, and several examples of its use are discussed.