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 [1].
On the Properties of GZ-Aggregates in Answer Set Programming
Authors: Mario Alviano, Nicola Leone
IJCAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Gelfond and Zhang recently proposed a new stable model semantics based on Vicious Circle Principle in order to improve the interpretation of logic programs with aggregates. A detailed complexity analysis of coherence testing and cautious reasoning under the new semantics highlighted similarities and differences versus mainstream stable model semantics for aggregates, which eventually led to the design of compilation techniques for implementing the new semantics on top of existing ASP solvers. ... Our previous paper [Alviano and Leone, 2015], honored with the Best Paper Award at the 31st International Conference on Logic Programming (ICLP 2015), explored this new semantics, reporting a detailed complexity analysis of coherence testing and cautious reasoning [Eiter and Gottlob, 1995], two of the main computational tasks in ASP. ... Table 1: Complexity of G-coherence testing and G-cautious reasoning. All complexity bounds are tight, and K denotes constant complexity. |
| Researcher Affiliation | Academia | Mario Alviano and Nicola Leone Department of Mathematics and Computer Science University of Calabria, Italy EMAIL |
| Pseudocode | No | The paper describes rewriting operations in textual steps (e.g., 'For each p...', 'For each rule r...') but does not contain clearly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | Yes | It is publicly available (http://alviano.net/software/g-stable-models/) and allows for experimenting with this newly proposed semantics. |
| Open Datasets | No | The paper is theoretical and uses abstract examples for illustration; it does not utilize or refer to any publicly available or open datasets for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not describe empirical experiments or dataset usage, thus no training/validation/test splits are mentioned. |
| Hardware Specification | No | The paper is theoretical and does not mention any specific hardware used for running experiments or computations. |
| Software Dependencies | No | The paper refers to 'existing ASP solvers' and 'modern ASP solvers' but does not provide specific software names with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup, hyperparameters, or system-level training settings. |