Summary of Relational Neurosymbolic Markov Models, by Lennert De Smet et al.
Relational Neurosymbolic Markov Models
by Lennert De Smet, Gabriele Venturato, Luc De Raedt, Giuseppe Marra
First submitted to arxiv on: 17 Dec 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 Sequential deep learning models have excelled in various artificial intelligence (AI) applications, but they often fail to guarantee the satisfaction of constraints necessary for trustworthy deployment. In contrast, neurosymbolic AI (NeSy) provides a formalism to enforce constraints in deep probabilistic models, but it scales exponentially on sequential problems. To overcome these limitations, we introduce relational neurosymbolic Markov models (NeSy-MMs), which integrate and provably satisfy relational logical constraints. We propose a strategy for inference and learning that combines approximate Bayesian inference, automated reasoning, and gradient estimation. Our experiments show that NeSy-MMs can solve problems beyond the current state-of-the-art in neurosymbolic AI while providing strong guarantees with respect to desired properties. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Sequential models are important in artificial intelligence (AI), but they often don’t work well when we need them to follow certain rules. This is a problem because we want our AI systems to be trustworthy and reliable. Neurosymbolic AI is one way to enforce these rules, but it doesn’t work well with long sequences of data. To solve this problem, we created a new type of model called relational neurosymbolic Markov models (NeSy-MMs). These models can learn and reason about complex rules while still working well with sequential data. |
Keywords
» Artificial intelligence » Bayesian inference » Deep learning » Inference
