Logic for problem-solving
Communications of the ACM
Progress report on program-understanding systems.
Progress report on program-understanding systems.
How to Upgrade Propositional Learners to First Order Logic: A Case Study
Machine Learning and Its Applications, Advanced Lectures
A Statistical Approach to Incremental Induction of First-Order Hierarchical Knowledge Bases
ILP '08 Proceedings of the 18th international conference on Inductive Logic Programming
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
Noise-resistant incremental relational learning using possible worlds
ILP'02 Proceedings of the 12th international conference on Inductive logic programming
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A framework for inductive inference in logic is presented: a Model Inference Problem is defined, and it is shown that problems of machine learning and program synthesis from examples can be formulated naturally as model inference problems. A general, incremental inductive inference algorithm for solving model inference problems is developed. This algorithm is based on Popper's methodology of conjectures and refutations [II]. The algorithm can be shown to identify in the limit [3] any model in a family of complexity classes of models, is most powerful of its kind, and is flexible enough to have been successfully implemented for several concrete domains. The Model Inference System is a Prolog implementation of this algorithm, specialized to infer theories in Horn form. It can infer axiomatizations of concrete models from a small number of facts in a practical amount of time.