Inductive Learning of Answer Set Programs from Noisy Examples
This work addresses the challenge of learning common-sense knowledge with defaults and exceptions from noisy data, which is important for applications in non-monotonic inductive logic programming, though it appears incremental.
The paper tackles the problem of learning answer set programs from noisy examples by introducing a noise-tolerant generalization of the learning from answer sets framework. The result shows that their ILASP3 system achieves higher accuracy than other ILP systems on many datasets, including a recent differentiable learning framework.
In recent years, non-monotonic Inductive Logic Programming has received growing interest. Specifically, several new learning frameworks and algorithms have been introduced for learning under the answer set semantics, allowing the learning of common-sense knowledge involving defaults and exceptions, which are essential aspects of human reasoning. In this paper, we present a noise-tolerant generalisation of the learning from answer sets framework. We evaluate our ILASP3 system, both on synthetic and on real datasets, represented in the new framework. In particular, we show that on many of the datasets ILASP3 achieves a higher accuracy than other ILP systems that have previously been applied to the datasets, including a recently proposed differentiable learning framework.