Summary of Semirings For Probabilistic and Neuro-symbolic Logic Programming, by Vincent Derkinderen et al.
Semirings for Probabilistic and Neuro-Symbolic Logic Programming
by Vincent Derkinderen, Robin Manhaeve, Pedro Zuidberg Dos Martires, Luc De Raedt
First submitted to arxiv on: 21 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| 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 provides a unified algebraic perspective on probabilistic logic programming (PLP), which integrates probabilistic models into programming languages based on logic. The field has evolved over 30 years, with various languages and frameworks developed for modeling, inference, and learning in PLP. Recent advancements have incorporated continuous distributions and neural networks, giving rise to neural-symbolic methods. By casting many PLP extensions within a common algebraic logic programming framework, this work provides a foundation for understanding the relationships between different approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a special way of combining computers and math problems. It’s called probabilistic logic programming (PLP). PLP helps us solve complex problems by combining two things: computer programs and mathematical rules. The paper shows that many different ways of doing this can be connected together using a simple algebraic framework. This makes it easier to understand how different approaches work together. |
Keywords
» Artificial intelligence » Inference
