Abstract
The modelling and reformulation of constraint networks are recognised as important problems. The task of automatically acquiring a constraint network formulation of a problem from a subset of its solutions and non-solutions has been presented in the literature. However, the choice of such a subset was assumed to be made independently of the acquisition process. We present an approach in which an interactive acquisition system actively selects a good set of examples. We show that the number of examples required to acquire a constraint network is significantly reduced using our approach.
| Original language | English |
|---|---|
| Pages (from-to) | 50-55 |
| Number of pages | 6 |
| Journal | IJCAI International Joint Conference on Artificial Intelligence |
| Publication status | Published - 2007 |
| Externally published | Yes |
| Event | 20th International Joint Conference on Artificial Intelligence, IJCAI 2007 - Hyderabad, India Duration: 6 Jan 2007 → 12 Jan 2007 |