Inductive logic programming
27 papers with code • 1 benchmarks • 2 datasets
Recent work on neuro-symbolic inductive logic programming has led to promising approaches that can learn explanatory rules from noisy, real-world data.
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.
The recent success of natural language understanding (NLU) systems has been troubled by results highlighting the failure of these models to generalize in a systematic and robust way.
We propose a new formal language for the expressive representation of probabilistic knowledge based on Answer Set Programming (ASP).
We present a fast and scalable algorithm to induce non-monotonic logic programs from statistical learning models.