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.
Multi-Agent Only Knowing on Planet Kripke
Authors: Guillaume Aucher, Vaishak Belle
IJCAI 2015 | Venue PDF | LLM Run Details | Input Tokens: 15,891 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,729 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 | We propose a new account based on Moss characteristic formulas, formulated for the usual Kripke semantics. This is shown to come with other benefits: the logic admits a group version of only knowing, and an operator for assessing the epistemic entrenchment of what an agent or a group only knows is de๏ฌnable. Finally, the multi-agent only knowing operator is shown to be expressible with the cover modality of classical modal logic, which then allows us to obtain a completeness result for a fragment of the logic. |
| Researcher Affiliation | Academia | Guillaume Aucher University of Rennes 1 INRIA Rennes, France EMAIL Vaishak Belle Dept. of Computer Science KU Leuven Leuven, Belgium EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the described methodology. |
| Open Datasets | No | The paper does not describe any experiments involving datasets, public or otherwise. |
| Dataset Splits | No | The paper does not describe any dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide any specific hardware details used for running experiments. |
| Software Dependencies | No | The paper does not list any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper does not describe any experimental setup details such as hyperparameters or training configurations, as it is a theoretical work. |