Stratified Negation in Datalog with Metric Temporal Operators
Authors: David J Tena Cucala, Przemysław A Wałęga, Bernardo Cuenca Grau, Egor Kostylev6488-6495
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We extend Datalog MTL Datalog with operators from metric temporal logic by adding stratified negation as failure. The new language provides additional expressive power for representing and reasoning about temporal data and knowledge in a wide range of applications. We consider models over the rational timeline, study their properties, and establish the computational complexity of reasoning. We show that, as in negation-free Datalog MTL, fact entailment in our language is PSPACE-complete in data and EXPSPACE-complete in combined complexity. |
| Researcher Affiliation | Academia | 1Department of Computer Science, University of Oxford 2Department of Informatics, University of Oslo |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | This is a theoretical paper focusing on language definition and complexity analysis; therefore, it does not involve training models on datasets. |
| Dataset Splits | No | This is a theoretical paper focusing on language definition and complexity analysis; therefore, it does not involve training models or dataset splits for validation. |
| Hardware Specification | No | This is a theoretical paper focusing on language definition and complexity analysis; therefore, it does not involve experiments that would require hardware specifications. |
| Software Dependencies | No | This is a theoretical paper focusing on language definition and complexity analysis; therefore, it does not involve experiments that would require specific software dependencies with version numbers. |
| Experiment Setup | No | This is a theoretical paper focusing on language definition and complexity analysis; therefore, it does not involve experimental setup details such as hyperparameters or training configurations. |