Summary of Towards Probabilistic Inductive Logic Programming with Neurosymbolic Inference and Relaxation, by Fieke Hillerstrom et al.
Towards Probabilistic Inductive Logic Programming with Neurosymbolic Inference and Relaxation
by Fieke Hillerstrom, Gertjan Burghouts
First submitted to arxiv on: 21 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes Propper, an inductive logic programming (ILP) method that addresses the limitation of many existing ILP methods in learning programs from probabilistic background knowledge. This is particularly useful when dealing with sensory data or neural networks that provide probabilistic outputs. By combining neurosymbolic inference, a continuous criterion for hypothesis selection (BCE), and a relaxation of the hypothesis constrainer (NoisyCombo), Propper can effectively handle flawed and probabilistic background knowledge. The results show that Propper outperforms binary ILP and statistical models such as Graph Neural Networks in learning relational patterns from noisy images using as few as 8 examples. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Propper is a new way to learn programs from uncertain data, like pictures or sensor readings. This can be useful for making decisions based on incomplete information. The researchers combined different ideas to create Propper, which does better than other methods in learning patterns from noisy images using just a few examples. |
Keywords
» Artificial intelligence » Inference
