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].
A Privacy Preserving Algorithm for Multi-Agent Planning and Search
Authors: Ronen Israel Brafman
IJCAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The main contribution of this paper is an enhanced version of the distributed forward-search planning framework of Nissim and Brafman that reveals less information than the original algorithm, and the first, to our knowledge, discussion and formal proof of privacy guarantees for distributed planning and search algorithms. and we provide the first formal treatment of privacy in the context of distributed planning algorithms. |
| Researcher Affiliation | Academia | Ronen I. Brafman Department of Computer Science Ben-Gurion University of the Negev Be er Sheva, Israel EMAIL |
| Pseudocode | Yes | Algorithm 1 MAFS for agent ϕi, Algorithm 2 process-message(m = s, gϕj(s), hϕj(s) ), Algorithm 3 expand(s), Algorithm 4 secure-process-message(m = s, gϕj(s), hϕj(s) ), Algorithm 5 secure-expand(s), Algorithm 6 virtual-send(s,ϕj) |
| Open Source Code | No | The paper does not include any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper is theoretical and does not use datasets for empirical evaluation. It refers to conceptual 'planning tasks' and 'planning domains' like Logistics as examples within its theoretical framework, but not as publicly accessible datasets for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments requiring dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe empirical experiments, therefore no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe an implemented system or empirical experiments, therefore no software dependencies with version numbers are mentioned. |
| Experiment Setup | No | The paper is theoretical and does not describe empirical experiments, therefore no experimental setup details like hyperparameters or training settings are provided. |