Abstract
Cost reduction of explanation-based learning (EBL) systems is studied using transformational analysis. In this method, the learning process is decomposed into a sequence of transformations from the problem solving to the learned rules. The structure of the problem solving is varied from the match process from learned rules; and search control rules and the optimization employed in the problem solving are ignored. Results on a set of known expensive-rule learning tasks show that such modifications can effectively eliminate the identified set of sources of expensiveness.
Original language | English |
---|---|
Pages | 1364 |
Number of pages | 1 |
State | Published - 1996 |
Event | Proceedings of the 1996 13th National Conference on Artificial Intelligence. Part 2 (of 2) - Portland, OR, USA Duration: 4 Aug 1996 → 8 Aug 1996 |
Conference
Conference | Proceedings of the 1996 13th National Conference on Artificial Intelligence. Part 2 (of 2) |
---|---|
City | Portland, OR, USA |
Period | 4/08/96 → 8/08/96 |