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  • Towards Probabilistic Inductive Logic Programming with Neurosymbolic Inference and Relaxation

  • FIEKE HILLERSTRÖM, GERTJAN BURGHOUTS
  • Journal:
    Theory and Practice of Logic Programming / Volume 24 / Issue 4 / July 2024
    Published online by Cambridge University Press:
    15 January 2025, pp. 628-643
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  • Many inductive logic programming (ILP) methods are incapable of learning programs from probabilistic background knowledge, for example, 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 (binary cross-entropy) 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.