Summary of On the Generalization Of Learned Constraints For Asp Solving in Temporal Domains, by Javier Romero et al.
On the generalization of learned constraints for ASP solving in temporal domains
by Javier Romero, Torsten Schaub, Klaus Strauch
First submitted to arxiv on: 29 Jan 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 The paper investigates how constraint learning can be applied to temporal reasoning problems in Answer Set Programming (ASP). Currently, solving dynamic problems in ASP involves duplicating variables and constraints for each time stamp, regardless of whether the representation is direct or indirect. This approach doesn’t account for the temporal relationships between different instances. The researchers explore whether a constraint learned for specific time steps can be generalized and reused at other time stamps, potentially enhancing solver performance on temporal problems. They propose a simple translation method that enables generalization of learned constraints to other time points. Additionally, they identify a property of temporal problems that allows all learned constraints to be generalized to all time steps. Many planning problems satisfy this property. The impact of adding these generalized constraints to an ASP solver is empirically evaluated. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how computers can solve complex problems involving time. Right now, solving these problems requires creating copies of the variables and rules for each moment in time, even if we’re not directly stating it that way. This approach doesn’t consider the relationships between different moments in time. The researchers ask whether a rule learned for one specific moment in time can be used again at other times. They suggest a simple way to translate the original problem into one that allows generalizing these rules. They also find a property of temporal problems that lets us generalize all rules to all moments in time, which is true for many planning problems. Finally, they test how adding these generalized rules improves computer solver performance. |
Keywords
» Artificial intelligence » Generalization » Translation
