Heterogeneous Facility Location with Limited Resources
Authors: Argyrios Deligkas, Aris Filos-Ratsikas, Alexandros A. Voudouris4966-4974
AAAI 2022 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | For each setting, we design deterministic and randomized strategyproof mechanisms that achieve a good approximation of the optimal social welfare, and complement these with nearly-tight impossibility results. |
| Researcher Affiliation | Academia | Argyrios Deligkas,1 Aris Filos-Ratsikas,2 Alexandros A. Voudouris3 1Department of Computer Science, Royal Holloway University of London 2Department of Computer Science, University of Liverpool 3School of Computer Science and Electronic Engineering, University of Essex EMAIL, aris.filosEMAIL, EMAIL |
| Pseudocode | Yes | Middle mechanism (MM) 1. Count the number nj of agents that approve each facility j {1, 2}. 2. Locate the most preferred facility at location 1 2, breaking ties arbitrarily. |
| Open Source Code | No | The paper is theoretical and does not mention releasing source code or provide links to a code repository for its methodology. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments on datasets, thus no information about public dataset availability is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve dataset splits (train/validation/test) as it does not conduct experiments. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental setup, thus no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe any experimental setup or software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup, hyperparameters, or training configurations. |