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Found 7 papers, 3 papers with code
Learning big logical rules by joining small rules
A major challenge in inductive logic programming is learning big rules.
Predicate Invention by Learning From Failures
Discovering novel high-level concepts is one of the most important steps needed for human-level AI.
Refinement Type Directed Search for Meta-Interpretive-Learning of Higher-Order Logic Programs
Building on the Meta-Interpretive Learning (MIL) framework, we show that type checking is able to prune large parts of the hypothesis space of programs.
Learning logic programs by explaining their failures
We show that fine-grained failure analysis allows for learning fine-grained constraints on the hypothesis space.
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.
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.
