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; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
Probabilistic Reasoning with Inconsistent Beliefs Using Inconsistency Measures
Authors: Nico Potyka, Matthias Thimm
IJCAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We illustrate our approach on several examples and show that it has both nice formal and computational properties. |
| Researcher Affiliation | Academia | Nico Potyka Fern Universit at in Hagen, Germany nico.potyka@Fern Uni-Hagen.de Matthias Thimm University of Koblenz-Landau, Germany EMAIL |
| Pseudocode | No | The paper describes mathematical formulations and optimization problems but does not provide pseudocode or algorithm blocks. |
| Open Source Code | Yes | The approach proposed in this paper has been implemented in Java and is available as open source2. 2tweetyproject.org |
| Open Datasets | No | The paper uses small, constructed knowledge bases for its examples, not publicly available datasets. Therefore, no information on public dataset access is provided. |
| Dataset Splits | No | The paper uses small, constructed knowledge bases for examples and does not mention training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide any specific hardware details used for running experiments. |
| Software Dependencies | No | The paper mentions that the approach |
| Experiment Setup | No | The paper focuses on the theoretical and computational properties of the generalized entailment problem but does not provide specific experimental setup details such as hyperparameters or system-level training settings. |