Summary of Answer Set Networks: Casting Answer Set Programming Into Deep Learning, by Arseny Skryagin et al.
Answer Set Networks: Casting Answer Set Programming into Deep Learning
by Arseny Skryagin, Daniel Ochs, Phillip Deibert, Simon Kohaut, Devendra Singh Dhami, Kristian Kersting
First submitted to arxiv on: 19 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG); Symbolic Computation (cs.SC)
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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 A novel neural-symbolic (NeSy) solver, called Answer Set Networks (ASN), is proposed to address the limitations of computing stable models and CPU-bound nature of state-of-the-art solvers in Answer Set Programming (ASP). By leveraging Graph Neural Networks (GNN), ASNs offer a scalable approach to Deep Probabilistic Logic Programming (DPPL). The translation of ASPs into ASNs and the efficient solving of encoded problems are demonstrated. Experimental evaluations show that ASNs outperform state-of-the-art CPU-bound NeSy systems on multiple tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to solve complex problems is being developed. It’s called Answer Set Networks, or ASNs. Right now, it’s hard to use a type of programming called Answer Set Programming because it takes too long and uses up too many computer resources. The new method uses something called Graph Neural Networks to make it faster and more efficient. This can help with things like training language models and guiding drones through the air safely. |
Keywords
» Artificial intelligence » Gnn » Translation
