First-Order Disjunctive Logic Programming vs Normal Logic Programming
Authors: Yi Zhou
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we study the expressive power of first-order disjunctive logic programming (DLP) and normal logic programming (NLP) under the stable model semantics. We show that, unlike the propositional case, first-order DLP is strictly more expressive than NLP. This result still holds even if auxiliary predicates are allowed, assuming NP = co NP. On the other side, we propose a partial translation from first-order DLP to NLP via unfolding and shifting, which suggests a sound yet incomplete approach to implement DLP via NLP solvers. We also identify some NLP definable subclasses, and conjecture to exactly capture NLP definability by unfolding and shifting. |
| Researcher Affiliation | Academia | Yi Zhou Artificial Intelligence Research Group School of Computing, Engineering and Mathematics University of Western Sydney, NSW, Australia |
| Pseudocode | No | The information is insufficient. The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The information is insufficient. The paper does not contain any statement about making source code publicly available. |
| Open Datasets | No | The information is insufficient. This is a theoretical paper and does not involve empirical experiments with datasets or provide access information for any. |
| Dataset Splits | No | The information is insufficient. This is a theoretical paper and does not describe dataset splits for training, validation, or testing. |
| Hardware Specification | No | The information is insufficient. This is a theoretical paper and does not describe hardware specifications for experiments. |
| Software Dependencies | No | The information is insufficient. This is a theoretical paper and does not list specific software dependencies with version numbers. |
| Experiment Setup | No | The information is insufficient. This is a theoretical paper and does not detail specific experimental setups or hyperparameters. |