Summary of Quantifying Over Optimum Answer Sets, by Giuseppe Mazzotta et al.
Quantifying over Optimum Answer Sets
by Giuseppe Mazzotta, Francesco Ricca, Mirek Truszczynski
First submitted to arxiv on: 14 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computational Complexity (cs.CC); Computation and Language (cs.CL)
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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 ASP Answer Set Programming with Quantifiers (ASP(Q)) is extended to tackle problems in the polynomial hierarchy (PH), but lacks a method for encoding problems requiring many oracle calls. This paper proposes an extension with weak constraints, which allows local and global optimization modeling. Various application scenarios demonstrate the new formalism’s capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers extend Answer Set Programming with Quantifiers (ASP(Q)) to solve problems in the polynomial hierarchy (PH). They also propose a method for encoding these problems in an elegant way. The new approach is shown through different application examples. |
Keywords
» Artificial intelligence » Optimization
