Explaining Answer-Set Programs with Abstract Constraint Atoms
Authors: Thomas Eiter, Tobias Geibinger
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We provide complexity results for checking and computing such justifications, and discuss how the semantic and syntactic approaches relate and can be jointly used to offer more insight. Our results contribute to a basis for explaining commonly used language features and thus increase accessibility and usability of ASP as an AI tool. |
| Researcher Affiliation | Academia | Thomas Eiter and Tobias Geibinger Knowledge-based Systems Group, Institute of Logic and Computation, TU Wien, Austria {thomas.eiter, tobias.geibinger}@tuwien.ac.at |
| Pseudocode | No | No pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not involve training models on publicly available datasets. |
| Dataset Splits | No | The paper does not describe experiments with dataset splits (training, validation, test). |
| Hardware Specification | No | The paper is theoretical and does not specify any hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not provide specific software dependencies with version numbers for experimental reproduction. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or training configurations. |