Summary of Probabilistic Answer Set Programming with Discrete and Continuous Random Variables, by Damiano Azzolini and Fabrizio Riguzzi
Probabilistic Answer Set Programming with Discrete and Continuous Random Variables
by Damiano Azzolini, Fabrizio Riguzzi
First submitted to arxiv on: 30 Sep 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 A novel extension to Answer Set Programming, Probabilistic Answer Set Programming under credal semantics (PASP), is proposed to incorporate uncertain information with discrete Bernoulli distributions. To address real-world scenarios requiring a combination of discrete and continuous random variables, the framework is extended to support continuous variables in Hybrid Probabilistic Answer Set Programming (HPASP). Two exact algorithms based on projected answer set enumeration and knowledge compilation are implemented and assessed, along with two approximate algorithms using sampling. Empirical results demonstrate that exact inference is feasible only for small instances, but knowledge compilation significantly improves performance, while sampling enables handling larger instances at the cost of increased memory requirements. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to work with uncertain information in computer programming. It’s called Probabilistic Answer Set Programming, or PASP for short. PASP lets us include uncertain facts that are either true or false with certain probabilities. This is useful when we’re dealing with real-world scenarios where things can’t be known for sure. To make it even more powerful, the authors extend PASP to also work with continuous variables, which can take on any value within a range. They then test different algorithms to see how well they work and find that some are better than others at handling larger amounts of data. |
Keywords
» Artificial intelligence » Inference » Semantics
