Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
First-Order Disjunctive Logic Programming vs Normal Logic Programming
Authors: Yi Zhou
IJCAI 2015 | Venue PDF | 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. |