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Found 10 papers, 4 papers with code
Learning programs with magic values
A magic value in a program is a constant symbol that is essential for the execution of the program but has no clear explanation for its choice.
Relational program synthesis with numerical reasoning
Our approach can identify numerical values in linear arithmetic fragments, such as real difference logic, and from infinite domains, such as real numbers or integers.
Learning logic programs by discovering where not to search
We use the constraints to bootstrap a constraint-driven ILP system.
Learning MDL logic programs from noisy data
Many inductive logic programming approaches struggle to learn programs from noisy data.
Can Meta-Interpretive Learning outperform Deep Reinforcement Learning of Evaluable Game strategies?
World-class human players have been outperformed in a number of complex two person games (Go, Chess, Checkers) by Deep Reinforcement Learning systems.
Beneficial and Harmful Explanatory Machine Learning
USML is demonstrated by a measurable increase in human performance of a task following provision to the human of a symbolic machine learned theory for task performance.
Learning logic programs by combining programs
The goal of inductive logic programming is to induce a logic program (a set of logical rules) that generalises training examples.
Learning logic programs by discovering higher-order abstractions
We introduce the higher-order refactoring problem, where the goal is to compress a logic program by discovering higher-order abstractions, such as map, filter, and fold.
Learning big logical rules by joining small rules
A major challenge in inductive logic programming is learning big rules.
Learning logic programs by finding minimal unsatisfiable subprograms
The goal of inductive logic programming (ILP) is to search for a logic program that generalises training examples and background knowledge.
