Foundations of logic programming; (2nd extended ed.)
Foundations of logic programming; (2nd extended ed.)
New Generation Computing - Selected papers from the international workshop on algorithmic learning theory,1990
Theories for mutagenicity: a study in first-order and feature-based induction
Artificial Intelligence - Special volume on empirical methods
Pharmacophore Discovery Using the Inductive Logic Programming System PROGOL
Machine Learning - Special issue on applications of machine learning and the knowledge discovery process
Predicate abstraction for software verification
POPL '02 Proceedings of the 29th ACM SIGPLAN-SIGACT symposium on Principles of programming languages
Foundations of Inductive Logic Programming
Foundations of Inductive Logic Programming
Carcinogenesis Predictions Using ILP
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
Improving the efficiency of inductive logic programming systems
Software—Practice & Experience
ECML'05 Proceedings of the 16th European conference on Machine Learning
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Inductive Logic Programming (ILP) [1] systems are general purpose learners that have had significant success on solving a number of relational problems, particularly from the biological domain [2,3,4,5]. However, the standard compression guided top-down search algorithm implemented in state of the art ILP systems like Progol [6] and Aleph [7] is not ideal for the Head Output Connected (HOC) class of learning problems. HOC is the broad class of predicates that have at least one output variable in the target concept. There are many relevant learning problems of this class such as arbitrary arithmetic functions and list manipulation predicates which are useful in areas such as automated software verification[8]. In this paper we present a special purpose ILP system to efficiently learn HOC predicates.