Summary of Understanding Domain-size Generalization in Markov Logic Networks, by Florian Chen et al.
Understanding Domain-Size Generalization in Markov Logic Networks
by Florian Chen, Felix Weitkämper, Sagar Malhotra
First submitted to arxiv on: 23 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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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 The paper investigates the generalization behavior of Markov Logic Networks (MLNs) across relational structures of varying sizes. It’s known that MLNs learned on a specific domain struggle to generalize well across domains of different sizes due to internal inconsistencies within the network. The authors quantify this inconsistency and bound it in terms of the variance of MLN parameters, showing that maximizing log-likelihood while minimizing parameter variance corresponds to natural notions of generalization. This theoretical framework applies to Exponential Random Graphs and other Markov networks. To achieve better generalization, controlling parameter variance is key, which can be achieved through regularization or Domain-Size Aware MLNs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how well a type of artificial intelligence called Markov Logic Networks (MLNs) work when they’re used in different situations. Right now, these networks are good at solving problems within a specific area, but they struggle to apply what they’ve learned to new and bigger areas. The researchers wanted to understand why this is happening. They found that the problem is due to inconsistencies within the network itself. To fix this issue, they developed a way to measure how well the network’s parameters are working together. This measurement helps them figure out which methods can be used to make the network more consistent and better at solving problems in different situations. |
Keywords
* Artificial intelligence * Generalization * Log likelihood * Regularization
