-Hammond, Davis
COMMENT
SUMMARY
Structural shape descriptions either provided explicitly by the user or generated automatically by the computer are often over or under constrained. This paper describes a method to debug over and under constrained shapes in LADDER descriptions using a novel active learning technique that generates its own near missed example shapes.
LADDER based systems require the domain designer to provide shape descriptions. An intuitive way to provide description would be the draw and have the computer understand automatically. However these descriptions are often imperfect because of the inability of the computer to understand the intent of the user. The authors developed a visual debugger, that first asks the system to draw a positive example. After this the system generates near-miss examples (one additional or one less constrain) to be classified by the user as negative and positive. One the basis of user classification it removes unintended constraints and adds required constraints. But for this the system first needs to generate near miss examples. An initial set of true constraints is captured. This list is kept small and relevant using a set of heuristics. Each time a positive classification is encountered, the system removes from the list any constraint that is not true of the system. For under-constrained figures we determine a set of constraints which are not in the description. We add the negation of each constraint one by one. If the user gives a negative classification, the constraint is added. Thus the shape description is incrementally perfected.
DISCUSSION
Describing shapes by drawing them is very important from an HCI perspective. This paper provides a method fro enabling users to do this accurately. I was concerned about the size of the initial list of constraints until the authors describe a way to prune this list to include only the relevant ones.
The system also omits disjunctive constraints. A complex shape could easily consist of a Boolean combination of constraints rather then being described by individual constraints. For example two shapes which are mirror images of each other and are laterally asymmetric might need disjunctive constraints to describe them.
Purely from a UI perspective, would it be better to provide a group of shapes (say 10-15) to be classified by the user at once, rather then presenting it one by one.
2 comments:
The disjunctive constraints notion is interesting, but it seems you'd be adding yet another layer onto the two of primitives->shapes. It seems more reasonable to keep constraints on a per-shape basis instead of also between-shapes because the algorithm would check a shape's constraints, check for other shapes that meet it's between-shape constraints, and then, if found, check the constraints of those constraints as well. Then vic versa.
Well the system follows the notion that many shapes at once would be overwhelming to the user. But yes may be showing 10-15 shapes at once would make the process faster.. but that depends upon the level of comfort people have with the system
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