Principles of artificial intelligence
Principles of artificial intelligence
ACM SIGKDD Explorations Newsletter
Algorithmic Program DeBugging
Foundations of Inductive Logic Programming
Foundations of Inductive Logic Programming
Learning Logical Definitions from Relations
Machine Learning
Carcinogenesis Predictions Using ILP
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
Refinement Operators Can Be (Weakly) Perfect
ILP '99 Proceedings of the 9th International Workshop on Inductive Logic Programming
Learning Logic Programs with Neural Networks
ILP '01 Proceedings of the 11th International Conference on Inductive Logic Programming
Relational learning as search in a critical region
The Journal of Machine Learning Research
Fast Theta-Subsumption with Constraint Satisfaction Algorithms
Machine Learning
QG/GA: a stochastic search for Progol
Machine Learning
Fast estimation of first-order clause coverage through randomization and maximum likelihood
Proceedings of the 25th international conference on Machine learning
Duce, an oracle-based approach to constructive induction
IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 1
On generalization and subsumption for ordered clauses
JSAI'05 Proceedings of the 2005 international conference on New Frontiers in Artificial Intelligence
AutoSPARQL: let users query your knowledge base
ESWC'11 Proceedings of the 8th extended semantic web conference on The semantic web: research and applications - Volume Part I
When does it pay off to use sophisticated entailment engines in ILP?
ILP'10 Proceedings of the 20th international conference on Inductive logic programming
ILP'10 Proceedings of the 20th international conference on Inductive logic programming
MC-TopLog: complete multi-clause learning guided by a top theory
ILP'11 Proceedings of the 21st international conference on Inductive Logic Programming
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Over the last decade Inductive Logic Programming systems have been dominated by use of top-down refinement search techniques. In this paper we re-examine the use of bottom-up approaches to the construction of logic programs. In particular, we explore variants of Plotkin's Relative Least General Generalisation (RLGG) which are based on subsumption relative to a bottom clause. With Plotkin's RLGG, clause length grows exponentially in the number of examples. By contrast, in the Golem system, the length of ij-determinate RLGG clauses were shown to be polynomially bounded for given values of i and j. However, the determinacy restrictions made Golem inapplicable in many key application areas, including the learning of chemical properties from atom and bond descriptions. In this paper we show that with Asymmetric Relative Minimal Generalisations (or ARMGs) relative to a bottom clause, clause length is bounded by the length of the initial bottom clause. ARMGs, therefore do not need the determinacy restrictions used in Golem. An algorithm is described for constructing ARMGs and this has been implemented in an ILP system called ProGolem which combines bottom-clause construction in Progol with a Golem control strategy which uses ARMG in place of determinate RLGG. ProGolem has been evaluated on several well-known ILP datasets. It is shown that ProGolem has a similar or better predictive accuracy and learning time compared to Golem on two determinate real-world applications where Golem was originally tested. Moreover, ProGolem was also tested on several non-determinate realworld applications where Golem is inapplicable. In these applications, ProGolem and Aleph have comparable times and accuracies. The experimental results also suggest that ProGolem significantly outperforms Aleph in cases where clauses in the target theory are long and complex.