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Found 25 papers, 14 papers with code
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.
Learning big logical rules by joining small rules
A major challenge in inductive logic programming is learning big rules.
Learning MDL logic programs from noisy data
Many inductive logic programming approaches struggle to learn programs from noisy data.
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.
Generalisation Through Negation and Predicate Invention
The ability to generalise from a small number of examples is a fundamental challenge in machine learning.
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.
Constraint-driven multi-task learning
Inductive logic programming is a form of machine learning based on mathematical logic that generates logic programs from given examples and background knowledge.
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.
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 where not to search
We use the constraints to bootstrap a constraint-driven ILP system.
Learning logic programs through divide, constrain, and conquer
We introduce an inductive logic programming approach that combines classical divide-and-conquer search with modern constraint-driven search.
Predicate Invention by Learning From Failures
Discovering novel high-level concepts is one of the most important steps needed for human-level AI.
Inductive logic programming at 30
Inductive logic programming (ILP) is a form of logic-based machine learning.
Learning logic programs by explaining their failures
We show that fine-grained failure analysis allows for learning fine-grained constraints on the hypothesis space.
Inductive logic programming at 30: a new introduction
Inductive logic programming (ILP) is a form of machine learning.
Learning programs by learning from failures
In this approach, an ILP system (the learner) decomposes the learning problem into three separate stages: generate, test, and constrain.
Knowledge Refactoring for Inductive Program Synthesis
We introduce the \textit{knowledge refactoring} problem, where the goal is to restructure a learner's knowledge base to reduce its size and to minimise redundancy in it.
Learning large logic programs by going beyond entailment
We argue that a key limitation of existing systems is that they use entailment to guide the hypothesis search.
Turning 30: New Ideas in Inductive Logic Programming
Common criticisms of state-of-the-art machine learning include poor generalisation, a lack of interpretability, and a need for large amounts of training data.
Forgetting to learn logic programs
To improve learning performance, we explore the idea of forgetting, where a learner can additionally remove programs from its BK.
Logical reduction of metarules
In general, derivation reduced sets of metarules outperforms subsumption and entailment reduced sets, both in terms of predictive accuracies and learning times.
Learning higher-order logic programs
Our theoretical results show that learning higher-order programs, rather than first-order programs, can reduce the textual complexity required to express programs which in turn reduces the size of the hypothesis space and sample complexity.
Playgol: learning programs through play
In this approach, a program induction system (the learner) is given a set of tasks and initial background knowledge.
