Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
C4.5: programs for machine learning
C4.5: programs for machine learning
Extracting Refined Rules from Knowledge-Based Neural Networks
Machine Learning
Knowledge-based artificial neural networks
Artificial Intelligence
Theories for mutagenicity: a study in first-order and feature-based induction
Artificial Intelligence - Special volume on empirical methods
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Machine Learning
Approximate Match of Rules Using Backpropagation Neural Networks
Machine Learning
Foundations of Inductive Logic Programming
Foundations of Inductive Logic Programming
Inductive Logic Programming: Techniques and Applications
Inductive Logic Programming: Techniques and Applications
Neural-Symbolic Learning System: Foundations and Applications
Neural-Symbolic Learning System: Foundations and Applications
Approximating the Semantics of Logic Programs by Recurrent Neural Networks
Applied Intelligence
FONN: Combining First Order Logic with Connectionist Learning
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
An Overview of Hybrid Neural Systems
Hybrid Neural Systems, revised papers from a workshop
Applying ILP to Diterpene Structure Elucidation from 13C NMR Spectra
ILP '96 Selected Papers from the 6th International Workshop on Inductive Logic Programming
BIBE '03 Proceedings of the 3rd IEEE Symposium on BioInformatics and BioEngineering
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
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Inductive Logic Programming (ILP) is a well-known machine learning technique for learning concepts from relational data. Nevertheless, ILP systems are not robust enough to noisy or unseen data in real world domains. Furthermore, in multi-class problems, if the example is not matched with any learned rules, it cannot be classified. This paper presents a novel hybrid learning method to alleviate this restriction by enabling Neural Networks to handle first-order logic programs directly. The proposed method, called First-Order Logical Neural Network (FOLNN), employs the standard feedforward neural network and integrates inductive learning from examples and background knowledge. We also propose a method for determining the appropriate variable substitution in FOLNN learning by using Multiple-Instance Learning (MIL). In the experiments, the proposed method has been evaluated on two first-order learning problems, i.e., the Finite Element Mesh Design and Mutagenesis and compared with the state-of-the-art, the PROGOL system. The experimental results show that the proposed method performs better than PROGOL.