First-Order Progression Size and Decidability Analyzed
A recent study published on arXiv delves into the analysis of first-order progression size and decidability within the realm of artificial intelligence. It highlights that while progression typically necessitates second-order logic, specific actions—including local-effect, normal, and acyclic—allow for first-order progression, which expands polynomially. The research employs Situation Calculus as its framework and identifies decidable fragments in two-variable first-order logic and universal theories with constants. Additionally, it emphasizes that size complexity is crucial for practical applications, with decidability assured when the knowledge base fits specific fragments.
Key facts
- Progression generally requires second-order logic.
- Local-effect, normal, and acyclic actions admit first-order progression.
- First-order progression for these classes grows polynomially.
- Two-variable first-order logic and universal theories with constants are decidable fragments.
- The framework used is Situation Calculus.
- The paper addresses size complexity for practical applications.
- Decidability is ensured when KB belongs to certain fragments.
- The study is from Computer Science > Artificial Intelligence.
Entities
Institutions
- arXiv