1997
Autores
Jorge, A; Brazdil, PB;
Publicação
INDUCTIVE LOGIC PROGRAMMING
Abstract
Many state-of-the-art ILP systems require large numbers of negative examples to avoid overgeneralization. This is a considerable disadvantage for many ILP applications, namely inductive program synthesis where relativelly small and sparse example sets are a more realistic scenario. Integrity constraints are first order clauses that can play the role of negative examples in an inductive process. One integrity constraint can replace a long list of ground negative examples. However, checking the consistency of a program with a set of integrity constraints usually involves heavy theorem-proving. We propose an efficient constraint satisfaction algorithm that applies to a wide variety of useful integrity constraints and uses a Monte Carlo strategy. It looks for inconsistencies by random generation of queries to the program. This method allows the use of integrity constraints instead of (or together with) negative examples. As a consequence programs to induce can be specified more rapidly by the user and the ILP system tends to obtain more accurate definitions. Average running times are not greatly affected by the use of integrity constraints compared to ground negative examples.
2001
Autores
Brazdil, P; Jorge, A;
Publicação
EPIA
Abstract
2005
Autores
Gama, J; Camacho, R; Brazdil, P; Jorge, A; Torgo, L;
Publicação
ECML
Abstract
2009
Autores
Gama, J; Costa, VS; Jorge, AM; Brazdil, P;
Publicação
Discovery Science
Abstract
1994
Autores
Brazdil, P; Jorge, A;
Publicação
ECAI
Abstract
2001
Autores
Brazdil, P; Jorge, A;
Publicação
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Abstract
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