Summary of Optimisation in Neurosymbolic Learning Systems, by Emile Van Krieken
Optimisation in Neurosymbolic Learning Systems
by Emile van Krieken
First submitted to arxiv on: 19 Jan 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 A neurosymbolic AI approach integrates deep learning with symbolic AI to address challenges in training neural networks, improving explainability and interpretability, and verifying system correctness. The abstract explores how symbolic languages can be combined with neural components to leverage background knowledge. One promising direction is fuzzy reasoning, which considers degrees of truth rather than binary concepts. Our research focuses on the interaction between different forms of fuzzy reasoning and learning, leading to surprising results like a connection to the Raven paradox. We also investigate using background knowledge in deployed models, developing a new neural network layer based on fuzzy reasoning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Neurosymbolic AI tries to mix two kinds of artificial intelligence: deep learning and symbolic thinking. This combination could make it easier to train neural networks, understand how they work, and check if they’re correct. The researchers looked at how we can use special languages to combine the symbolic and neural parts. One idea is called fuzzy reasoning, where we consider things being partly true instead of just true or false. They found some surprising results, like a connection to the Raven paradox, which says that when we see something green (like an apple), it confirms our understanding that ravens are black. The study also looked at how we can use background knowledge in models after they’re trained and developed a new neural network layer based on fuzzy reasoning. |
Keywords
* Artificial intelligence * Deep learning * Neural network
