Interactive Constraint Acquisition–Position Paper

In many practical applications users often find it difficult to articulate their constraints. While users can recognize examples of where a constraint should be satisfied or violated, they cannot articulate the constraint itself. In these situations we would like the computer to take an active role in acquiring the user's constraints. In this paper we present an approach to interactive constraint acquisition based on techniques from the field of machine learning. Constraint acquisition is modeled as search through a "hypothesis space" of constraints over which specific-to-general ordering is known We have begun studying the issues involved in interactively acquiring constraints. We describe our basic approach in the context of a simple example involving the acquisition of a placement constraint. Finally, we lay out a program for further study, outlining our research agenda.