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..
Modeling and Reasoning about NTU Games via Answer Set Programming
Authors: Giovanni Amendola, Gianluigi Greco, Nicola Leone, Pierfrancesco Veltri
IJCAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The computational complexity of the proposed framework is studied, in particular, by focusing on the core as the prototypical solution concept. A system supporting the basic reasoning tasks arising therein is also made available, and results of experimental activity are discussed. |
| Researcher Affiliation | Academia | Giovanni Amendola, Gianluigi Greco, Nicola Leone, and Pierfrancesco Veltri DEMACS University of Calabria, Italy EMAIL |
| Pseudocode | No | The paper describes procedures and computational steps but does not include any clearly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | Yes | The system prototype and further notes on its usage are also available at http://ntu2dlv.altervista.org/. |
| Open Datasets | No | The system has been tested on different data sets of randomly-generated graphs, for normal, uniform and power-law distributions of node degrees. For each given distribution and desired number of nodes, 3 graphs have been generated and average times are discussed. The data generator along with a user guide can be downloaded from the system web-site. |
| Dataset Splits | No | The paper discusses experiments on 'randomly-generated graphs' and reports 'average times', but does not specify training, validation, or test dataset splits. |
| Hardware Specification | Yes | Tests have been carried out on an Intel Core i7-4710HQ, 2.50 GHz, with 16 Gb Ram, running Linux Operating System |
| Software Dependencies | No | The system prototype, named ntu2DLV, has been implemented on top of the well-known answer set programming DLV reasoner [Leone et al., 2006]. In the following, we discuss the architecture of the system and results of some experimental activities we have conducted on it. ...interactions with the DLV system via the DLVWrapper library [Ricca, 2003]: |
| Experiment Setup | Yes | Tests have been carried out on an Intel Core i7-4710HQ, 2.50 GHz, with 16 Gb Ram, running Linux Operating System; for each test we allowed a maximum running time of 1800 seconds. ...The system has been tested on different data sets of randomly-generated graphs, for normal, uniform and power-law distributions of node degrees. For each given distribution and desired number of nodes, 3 graphs have been generated and average times are discussed. |