Towards Probabilistic Inductive Logic Programming with Neurosymbolic Inference and Relaxation

• URL: http://arxiv.org/abs/2408.11367v1
• Date: Wed, 21 Aug 2024 06:38:49 GMT
• Title: Towards Probabilistic Inductive Logic Programming with Neurosymbolic Inference and Relaxation
• Authors: Fieke Hillerstrom, Gertjan Burghouts,
• Abstract summary: We propose Propper, which handles flawed and probabilistic background knowledge. For relational patterns in noisy images, Propper can learn programs from as few as 8 examples. It outperforms binary ILP and statistical models such as a Graph Neural Network.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Many inductive logic programming (ILP) methods are incapable of learning programs from probabilistic background knowledge, e.g. coming from sensory data or neural networks with probabilities. We propose Propper, which handles flawed and probabilistic background knowledge by extending ILP with a combination of neurosymbolic inference, a continuous criterion for hypothesis selection (BCE) and a relaxation of the hypothesis constrainer (NoisyCombo). For relational patterns in noisy images, Propper can learn programs from as few as 8 examples. It outperforms binary ILP and statistical models such as a Graph Neural Network.

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