Abstract
Normal logic programs are usually shorter and easier to
write and understand than definite logic programs. As a consequence,
it is worth investigating their learnability, if Inductive Logic Program-
ming is to be proposed as an alternative tool for software development
and Software Engineering at large. In this paper we present an exten-
sion of the ILP system TRACY, called TRACY-not, able to learn normal
logic programs. The method is proved to be sound, in the sense that
it outputs a program which is complete and consistent w.r.t.the ex-
amples, and complete, in the sense that it does find a solution when it
exists. Compared to learning systems based on extensionality,TRACY
and TRACY not are less dependent on the kind and number of training
examples, which is due to the intensional evaluation of the hypothe-
ses and, for TRACY-not, to the possibility to have restricted hypothesis
spaces through the use of negation.
Lingua originale | Inglese |
---|---|
Titolo della pubblicazione ospite | Advances in inductive logic programming |
Editore | IOS Press |
Pagine | 107-123 |
Numero di pagine | 17 |
ISBN (stampa) | 9051992424 |
Stato di pubblicazione | Pubblicato - 1 gen 1996 |
Keywords
- Artificial Intelligence
- Machine Learning
- Inductive Logic Programming