Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Strong Syntax Splitting for Iterated Belief Revision
Authors: Gabriele Kern-Isberner, Gerhard Brewka
IJCAI 2017 | Venue PDF | LLM Run Details | Input Tokens: 17,096 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,496 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper we generalize syntax splitting from logical sentences to epistemic states, a step which is necessary to cover iterated revision. The generalization is based on the notion of marginalization of epistemic states. Furthermore, we study epistemic syntax splitting in the context of ordinal conditional functions. Our approach substantially generalizes the semantical treatment of (P) in terms of faithful preorders recently presented by Peppas and colleagues. |
| Researcher Affiliation | Academia | Gabriele Kern-Isberner1 and Gerhard Brewka2 1Fakult at f ur Informatik, Technische Universit at Dortmund, Germany 2Institut f ur Informatik, Universit at Leipzig, Germany EMAIL EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | The paper does not describe any empirical experiments involving datasets, nor does it provide access information for any publicly available datasets. |
| Dataset Splits | No | The paper does not describe any empirical experiments involving datasets or data splits. |
| Hardware Specification | No | The paper does not provide specific hardware details used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers needed to replicate the experiment. |
| Experiment Setup | No | The paper does not describe any empirical experiments or their setup, thus no hyperparameter values or training configurations are provided. |