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].
Adversarial Contention Resolution Games
Authors: Giorgos Chionas, Bogdan S. Chlebus, Dariusz R. Kowalski, Piotr Krysta
IJCAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study contention resolution (CR) on a shared channel modelled as a game with selfish players. There are n agents and the adversary chooses some k n of them as players. Each participating player in a CR game has a packet to transmit. A transmission is successful if it is performed as the only one at a round. Each player aims to minimize its packet latency. We introduce the notion of adversarial equilibrium (AE), which incorporates adversarial selection of players. We develop efficient deterministic communication algorithms that are also AE. We characterize the price of anarchy in the CR games with respect to AE. |
| Researcher Affiliation | Academia | 1Department of Computer Science, University of Liverpool, UK 2School of Computer and Cyber Sciences, Augusta University, GA, USA |
| Pseudocode | Yes | Algorithm 1: Noisy GTRR(n), player i; Algorithm 2: Half Size(n), player i; Algorithm 3: Alt Rec(n), player i |
| Open Source Code | No | The paper does not provide any information about the availability of open-source code for the described methodology. |
| Open Datasets | No | This is a theoretical paper focusing on algorithm design and proofs, not empirical experiments involving datasets. Therefore, there is no mention of a dataset used for training. |
| Dataset Splits | No | This is a theoretical paper focusing on algorithm design and proofs, not empirical experiments involving datasets. Therefore, there is no mention of validation splits. |
| Hardware Specification | No | The paper does not provide any specific hardware details used for running computations or experiments. |
| Software Dependencies | No | The paper does not provide any specific software dependencies or version numbers needed to replicate the work. |
| Experiment Setup | No | This is a theoretical paper focusing on algorithm design and proofs, not empirical experiments. Therefore, there is no mention of an experimental setup including hyperparameters or system-level training settings. |