Summary of Towards Projected and Incremental Pseudo-boolean Model Counting, by Suwei Yang et al.
Towards Projected and Incremental Pseudo-Boolean Model Counting
by Suwei Yang, Kuldeep S. Meel
First submitted to arxiv on: 19 Dec 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 The paper introduces PBCount2, an exact pseudo-Boolean (PB) model counter that can handle both projected and incremental settings. This is a significant advancement in the field of PB model counting, which has seen limited attention due to the flexibility offered by PB formulas. The proposed counter utilizes the Least Occurrence Weighted Min Degree (LOW-MD) computation ordering heuristic for projected model counting and a cache mechanism for incremental model counting. Evaluation results show that PBCount2 outperforms competing methods in both projected (1.40x) and incremental (1.18x) model counting. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new tool, PBCount2, to help count the number of ways something can be true based on a set of rules. This is useful because it can handle different types of situations where some parts are already decided. The new tool uses two special tricks: one for dealing with projected scenarios and another for incremental ones. It beats other tools in doing these tasks, making it more efficient. |
Keywords
» Artificial intelligence » Attention
