📝 Original Paper Info
- Title: On Conformant Planning and Model-Checking of $ exists^* forall^*$ Hyperproperties
- ArXiv ID: 2512.23324
- Date: 2025-12-29
- Authors: Raven Beutner, Bernd Finkbeiner
📝 Abstract
We study the connection of two problems within the planning and verification community: Conformant planning and model-checking of hyperproperties. Conformant planning is the task of finding a sequential plan that achieves a given objective independent of non-deterministic action effects during the plan's execution. Hyperproperties are system properties that relate multiple execution traces of a system and, e.g., capture information-flow and fairness policies. In this paper, we show that model-checking of $\exists^*\forall^*$ hyperproperties is closely related to the problem of computing a conformant plan. Firstly, we show that we can efficiently reduce a hyperproperty model-checking instance to a conformant planning instance, and prove that our encoding is sound and complete. Secondly, we establish the converse direction: Every conformant planning problem is, itself, a hyperproperty model-checking task.
💡 Summary & Analysis
1. **Importance of Data Augmentation:** Crucial for improving model performance in data-scarce scenarios, akin to growing plants under varied conditions.
2. **Application of Various Techniques:** Using methods like rotation, flipping, and scaling to transform image data, similar to using diverse ingredients while cooking.
3. **Performance Enhancement:** Helps models better handle a variety of inputs, much like how athletes train in different conditions to perform well.
📄 Full Paper Content (ArXiv Source)
1. **Importance of Data Augmentation:** Crucial for improving model performance in data-scarce scenarios, akin to growing plants under varied conditions.
2. **Application of Various Techniques:** Using methods like rotation, flipping, and scaling to transform image data, similar to using diverse ingredients while cooking.
3. **Performance Enhancement:** Helps models better handle a variety of inputs, much like how athletes train in different conditions to perform well.
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