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..
Strong Inconsistency in Nonmonotonic Reasoning
Authors: Gerhard Brewka, Matthias Thimm, Markus Ulbricht
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We investigate the complexity of various related reasoning problems and present a generic algorithm for computing minimal strongly inconsistent subsets of a knowledge base. We also demonstrate the potential of our new notion for applications, focusing on repair and inconsistency measurement.Computational aspects of strong inconsistency, including a complexity analysis and a generic algorithm for computing strongly inconsistent subsets, are studied in Section 4.a detailed evaluation of this algorithm is left for future work. |
| Researcher Affiliation | Academia | 1Department of Computer Science, Leipzig University, Germany 2Institute for Web Science and Technologies (We ST), University of Koblenz-Landau, Germany |
| Pseudocode | Yes | Algorithm 1: A generic algorithm for computing SI(K) |
| Open Source Code | No | No statement explicitly providing concrete access to source code for the methodology (e.g., a repository link or an explicit code release statement) was found. A link to an extended version of the paper for proofs is provided, but not for code. |
| Open Datasets | No | The paper focuses on theoretical development and does not use or reference any datasets for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with training, validation, or test dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any specific hardware used for running experiments. |
| Software Dependencies | No | The paper is theoretical and does not provide specific software dependencies with version numbers (e.g., libraries, solvers) for replicating the work. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with specific hyperparameters, training configurations, or system-level settings. |