mlsanityData to 7 Oct 2026

Towards Probabilistic Inductive Logic Programming with Neurosymbolic Inference and Relaxation

arXiv 2408.11367 ↗First posted 21 Aug 2024cs.AI cs.LGcited by 5

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Abstract (arXiv)

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.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 16 of its 31 references that are in the map. First posted on arXiv 21 Aug 2024.

Builds on

16 of 31 references.

References · 16

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  11. 2021Inductive logic programming at 30cited by 46
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4 more
  1. 2020Abductive Knowledge Induction From Raw Datacited by 38
  2. 2023Learning MDL logic programs from noisy datacited by 15
  3. 2013Structure Learning of Probabilistic Logic Programs by Searching the Clause Spacecited by 10
  4. 2023V-LoL: A Diagnostic Dataset for Visual Logical Learningcited by 8

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 4; 2026: 1.

5 papers.

  1. 2025GLIDR: Graph-Like Inductive Logic Programming with Differentiable Reasoningcited by 1
  2. 2025Efficient rule induction by ignoring pointless rulescited by 0
  3. 2025Honey, I shrunk the hypothesis space (through logical preprocessing)cited by 0
  4. 2025Symmetry breaking for inductive logic programmingcited by 0
  5. 2026Explaining Neural Networks in Preference Learning: a Post-hoc Inductive Logic Programming Approachcited by 0