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..
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. |