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..
An ASP Semantics for Default Reasoning with Constraints
Authors: Pedro Cabalar, Roland Kaminski, Max Ostrowski, Torsten Schaub
IJCAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We implemented our approach (see [LC2CASP, 2016]) as an extension of the CASP solver CLINGCON 3 [Banbara et al., 2016]. Our system computes the stable models of an LCprogram by implementing a polynomial-size variant of the translation described in the previous section. ... The above LCprogram has 4 stable models, all assigning 1 to q(1) according to the default expressed in Line 5. However, once &assign { q(1) := 4 }. is added, the default is overwritten, and we obtain 18 models, yet all assigning 4 to q(1). |
| Researcher Affiliation | Academia | 1University of Corunna, Spain 2University of Potsdam, Germany 3INRIA, France |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. It provides formal logical definitions and transformations, but not in a pseudocode format. |
| Open Source Code | Yes | Our system along with several examples and additional material is available at [LC2CASP, 2016]. [LC2CASP, 2016] is listed as http://www.cs.uni-potsdam.de/lc2casp, 2016. |
| Open Datasets | No | The paper does not use external datasets in the traditional sense for training. It uses the 8-queens puzzle as an example, which is a well-known problem defined by its rules, not a dataset with concrete access information. |
| Dataset Splits | No | The paper does not describe dataset splits for training, validation, or testing, as it does not use a traditional dataset for empirical evaluation. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the system or experiments. |
| Software Dependencies | Yes | We implemented our approach ... as an extension of the CASP solver CLINGCON 3 [Banbara et al., 2016]. |
| Experiment Setup | Yes | For illustration, consider the HTC-program in (1) to (4) expressed as an LC-program: 1 n(1..8). 2 :not &distinct { q(X) : n(X) }. 3 :&sum { q(X); -q(Y) } = X-Y, n(X), n(Y), X != Y. 4 :&sum { q(X); -q(Y) } = Y-X, n(X), n(Y), X != Y. 5 &assign { q(1) := 1 } :not &sum { q(1) } != 1. 6 &assign { q(X) := 1..n } :n(X), X > 1. This provides the concrete program rules used for the example. |