Strong Inconsistency in Nonmonotonic Reasoning
Authors: Gerhard Brewka, Matthias Thimm, Markus Ulbricht
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | 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. |