timid acquisition of constraint satisfaction problems
In this paper we view interactive constraint acquisition as the process of learning constraints from examples and focus on the roles played by both the user and the system during an interactive session. We consider our user as a teacher who provides positive examples to an automated constraint acquisition system. Each positive example represents a solution to the target constraint network we are trying to acquire. In this paper we compare a number of ways in which users can choose examples to be presented to a constraint acquisition system and identify the best strategy for the user to adopt. We recognize that not every user will naturally be able to assume the best profile and therefore present an assistant that can help a user construct good examples. We show that the assistant helps, in a significant manner, a human user trying to describe a target constraint network using a very small number of examples.
constraint satisfaction machine learning