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].
Reasoning about Cognitive Trust in Stochastic Multiagent Systems
Authors: Xiaowei Huang, Marta Kwiatkowska
AAAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study the complexity of the automated verification problem and, while the general problem is undecidable, we identify restrictions on the logic and the system that result in decidable, or even tractable, subproblems. |
| Researcher Affiliation | Academia | Xiaowei Huang, Marta Kwiatkowska Department of Computer Science University of Oxford |
| Pseudocode | No | The paper describes formal definitions and logic syntax but does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating that source code for the described methodology is publicly available. |
| Open Datasets | No | The paper presents a theoretical framework and does not involve empirical experiments with datasets. |
| Dataset Splits | No | The paper is theoretical and does not involve experimental validation with dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any hardware used for running experiments. |
| Software Dependencies | No | The paper describes a theoretical framework and does not specify software dependencies with version numbers for experimental reproducibility. |
| Experiment Setup | No | The paper describes a theoretical framework and does not provide details of an experimental setup, such as hyperparameters or training configurations. |