Abstract
We investigate using the Mercury language to implement
and design ILP algorithms, presenting our own ILP system IMP. Mer-
cury provides faster execution than Prolog. Since Mercury is a purely
declarative language, run-time assertion of induced clauses is prohibited.
Instead IMP uses a problem-specific interpreter of ground representa-
tions of induced clauses. The interpreter is used both for cover testing
and bottom clause generation. The Mercury source for this interpreter
is generated automatically from the user’s background knowledge using
Moose, a Mercury parser generator. Our results include some encourag-
ing results on IMP’s cover testing speed, but overall IMP is still generally
a little slower than ALEPH.
and design ILP algorithms, presenting our own ILP system IMP. Mer-
cury provides faster execution than Prolog. Since Mercury is a purely
declarative language, run-time assertion of induced clauses is prohibited.
Instead IMP uses a problem-specific interpreter of ground representa-
tions of induced clauses. The interpreter is used both for cover testing
and bottom clause generation. The Mercury source for this interpreter
is generated automatically from the user’s background knowledge using
Moose, a Mercury parser generator. Our results include some encourag-
ing results on IMP’s cover testing speed, but overall IMP is still generally
a little slower than ALEPH.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 16th International Conference on Inductive Logic Programming (ILP 2006) |
| Subtitle of host publication | Lecture Notes in Artificial Intelligence |
| Publisher | Springer |
| Pages | 199-213 |
| Number of pages | 15 |
| Volume | 4455 |
| DOIs | |
| Publication status | Published - 2007 |