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 [1].
Only Knowing Meets Common Knowledge
Authors: Vaishak Belle, Gerhard Lakemeyer
IJCAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this work, we lift that serious limitation to obtain a first-order language with only knowing and common knowledge, allowing us to study the interaction between these notions for the very first time. By adding a simple form of public announcement, we then demonstrate how the muddy children puzzle can be cast in terms of logical implications given what is only known initially. |
| Researcher Affiliation | Academia | Vaishak Belle Dept. of Computer Science KU Leuven Belgium EMAIL Gerhard Lakemeyer: Dept. of Computer Science RWTH Aachen University Germany EMAIL :Also affiliated with the University of Toronto. |
| Pseudocode | No | The paper defines a formal logic with syntax and semantic rules but does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not mention providing open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not use any datasets for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not involve any data splits for training, validation, or testing. |
| Hardware Specification | No | The paper is purely theoretical and does not describe any experiments that would require hardware specifications. |
| Software Dependencies | No | The paper is theoretical and describes a logical system, not a software implementation. Therefore, no software dependencies with version numbers are mentioned. |
| Experiment Setup | No | The paper is theoretical and does not include any experimental setup details such as hyperparameters or training settings. |