A Prolog technology theorem prover: implementation by an extended Prolog computer
Journal of Automated Reasoning
Interactive Concept-Learning and Constructive Induction by Analogy
Machine Learning
Interactive theory revision: an inductive logic programming approach
Interactive theory revision: an inductive logic programming approach
Automated Refinement of First-Order Horn-Clause Domain Theories
Machine Learning
Algorithmic Program DeBugging
Finding Hypotheses from Examples by Computing the Least Generalization of Bottom Clauses
DS '98 Proceedings of the First International Conference on Discovery Science
DS '98 Proceedings of the First International Conference on Discovery Science
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
Which Hypotheses Can Be Found with Inverse Entailment?
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
Induction, Abduction, and Consequence-Finding
ILP '01 Proceedings of the 11th International Conference on Inductive Logic Programming
Induction on Failure: Learning Connected Horn Theories
LPNMR '09 Proceedings of the 10th International Conference on Logic Programming and Nonmonotonic Reasoning
Logic-based representation, reasoning and machine learning for event recognition
Proceedings of the Fourth ACM International Conference on Distributed Event-Based Systems
Discovering rules by meta-level abduction
ILP'09 Proceedings of the 19th international conference on Inductive logic programming
Chess revision: acquiring the rules of chess variants through FOL theory revision from examples
ILP'09 Proceedings of the 19th international conference on Inductive logic programming
Automatic revision of metabolic networks through logical analysis of experimental data
ILP'09 Proceedings of the 19th international conference on Inductive logic programming
Hypothesizing about causal networks with positive and negative effects by meta-level abduction
ILP'10 Proceedings of the 20th international conference on Inductive logic programming
Variation of background knowledge in an industrial application of ILP
ILP'10 Proceedings of the 20th international conference on Inductive logic programming
The need for ancestor resolution when answering queries in horn clause logic
ICLP'05 Proceedings of the 21st international conference on Logic Programming
Inducing causal laws by regular inference
ILP'05 Proceedings of the 15th international conference on Inductive Logic Programming
Machine learning for systems biology
ILP'05 Proceedings of the 15th international conference on Inductive Logic Programming
Does multi-clause learning help in real-world applications?
ILP'11 Proceedings of the 21st 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
Machine learning a probabilistic network of ecological interactions
ILP'11 Proceedings of the 21st international conference on Inductive Logic Programming
Learning Recursive Theories in the Normal ILP Setting
Fundamenta Informaticae
Completing causal networks by meta-level abduction
Machine Learning
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The main real-world applications of Inductive Logic Programming (ILP) to date involve the "Observation Predicate Learning" (OPL) assumption, in which both the examples and hypotheses define the same predicate. However, in both scientific discovery and language learning potential applications exist in which OPL does not hold. OPL is ingrained within the theory and performance testing of Machine Learning. A general ILP technique called "Theory Completion using Inverse Entailment" (TCIE) is introduced which is applicable to non-OPL applications. TCIE is based on inverse entailment and is closely allied to abductive inference. The implementation of TCIE within Progol5.0 is described. The implementation uses contra-positives in a similar way to Stickel's Prolog Technology Theorem Prover. Progol5.0 is tested on two different data-sets. The first dataset involves a grammar which translates numbers to their representation in English. The second dataset involves hypothesising the function of unknown genes within a network of metabolic pathways. On both datasets near complete recovery of performance is achieved after relearning when randomly chosen portions of background knowledge are removed. Progol5.0's running times for experiments in this paper were typically under 6 seconds on a standard laptop PC.