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Inductive Learning of Answer Set Programs from Noisy Examples
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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.
Forward citations
Cited by 2 Pith papers
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Defining neurosymbolic AI
Neurosymbolic inference is defined as a Lebesgue integral over interpretations of the product of a logical selection function and a parametrized belief function, unifying many existing systems.
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Bridging Logic Programming and Deep Learning for Explainability through ILASP
A research plan proposes pairing neural networks with ILP systems so that AI predictions come with human-readable logical rules, with early tests in weather, law, and biology.
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