Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Strong Equivalence for Epistemic Logic Programs Made Easy
Authors: Wolfgang Faber, Michael Morak, Stefan Woltran2809-2816
AAAI 2019 | Venue PDF | LLM Run Details | Input Tokens: 19,357 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,763 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we consider a simpler, more direct characterization that is directly applicable to the language used in state-of-the-art ELP solvers. This also allows us to give tight complexity bounds, showing that strong equivalence for ELPs remains co NP-complete, as for ASP. We further use our results to provide syntactic characterizations for tautological rules and rule subsumption for ELPs. |
| Researcher Affiliation | Academia | Wolfgang Faber, Michael Morak Alpen-Adria-Universit at Klagenfurt Klagenfurt, Austria Stefan Woltran TU Wien Vienna, Austria |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. It focuses on formal definitions, theorems, and proofs related to strong equivalence in logic programs. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. It mentions existing ELP solving systems but does not offer code for its own theoretical contributions. |
| Open Datasets | No | The paper is theoretical and does not use datasets for training, validation, or testing. |
| Dataset Splits | No | The paper is theoretical and does not use datasets, therefore no training/test/validation dataset splits are provided. |
| Hardware Specification | No | The paper is theoretical and does not describe any experiments, thus no specific hardware details are provided. |
| Software Dependencies | No | The paper is theoretical and does not describe any experiments, thus no specific ancillary software details with version numbers are provided for replication. |
| Experiment Setup | No | The paper is theoretical and does not describe any experiments, thus no specific experimental setup details like hyperparameters or training configurations are provided. |