Summary of Symbolic Parameter Learning in Probabilistic Answer Set Programming, by Damiano Azzolini et al.
Symbolic Parameter Learning in Probabilistic Answer Set Programming
by Damiano Azzolini, Elisabetta Gentili, Fabrizio Riguzzi
First submitted to arxiv on: 16 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 This abstract proposes two algorithms for learning parameters in probabilistic logic programs, a crucial task in Statistical Relational Artificial Intelligence. Given observations in the form of interpretations, the goal is to learn probabilities that maximize the interpretation probabilities. The first algorithm uses a constrained optimization solver while the second implements Expectation Maximization. Experimental results show that these proposals outperform existing methods based on projected answer set enumeration in terms of solution quality and execution time. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops two new algorithms for learning parameters in probabilistic logic programs. It’s like trying to figure out how likely certain things are true, given some information we already know. The algorithms work by finding special equations that represent the likelihoods of different interpretations. One algorithm uses a pre-made optimization tool, while the other is based on the Expectation Maximization method. Tests show that these new methods do better than previous approaches in terms of getting good answers and being fast. |
Keywords
» Artificial intelligence » Optimization
