Summary of Fast Inference For Probabilistic Answer Set Programs Via the Residual Program, by Damiano Azzolini and Fabrizio Riguzzi
Fast Inference for Probabilistic Answer Set Programs via the Residual Program
by Damiano Azzolini, Fabrizio Riguzzi
First submitted to arxiv on: 14 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Logic in Computer Science (cs.LO)
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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 approach to accelerate probabilistic reasoning in Probabilistic Answer Set Programs (PASP) is proposed. By identifying and removing redundant components, computation time can be drastically reduced. This paper explores the potential of SLG resolution algorithms in producing residual programs that can be leveraged for inference purposes. Experimental results on graph datasets demonstrate a significant speedup in inference processing. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to make computers faster at understanding probability is discovered. Some parts of computer programs are not important, but they take up too much time and memory. Researchers found a way to remove these unnecessary parts, making the computer run much quicker. This can help with big tasks that require lots of computation. |
Keywords
» Artificial intelligence » Inference » Probability
