Multi-Party Campaigning
Authors: Martin Koutecký, Nimrod Talmon5506-5513
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our algorithmic results rely on formulating the problem of finding an optimal strategy as sentences of Presburger arithmetic that are short and only involve small coefficients, which we show is fixed-parameter tractable indeed, one of our contributions is a general result regarding fixed-parameter tractability of Presburger arithmetic that might be useful in other settings.We devise efficient algorithms for finding optimal strategies for agents in these models, by a reduction to optimization over satisfying assignments of formulas in Presburger arithmetic. |
| Researcher Affiliation | Academia | Computer Science Institute, Charles University, Prague, Czech Republic, Ben-Gurion Univesity, Be er Sheva, Israel |
| Pseudocode | Yes | Figure 1: General Structure of φ. Figure 2: A modular and slightly more general version of Φ. |
| Open Source Code | No | The paper does not include any explicit statement about releasing source code for the described methodology, nor does it provide a direct link to a code repository or mention code in supplementary materials. |
| Open Datasets | No | The paper is theoretical and focuses on algorithmic complexity; it does not utilize or refer to any publicly available or open datasets for empirical evaluation. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments involving data; therefore, it does not provide training/test/validation dataset splits. |
| Hardware Specification | No | The paper focuses on theoretical algorithmic results and does not describe any specific hardware used to run experiments. |
| Software Dependencies | No | The paper does not provide specific software names with version numbers needed to replicate any experimental results, as it is a theoretical paper. |
| Experiment Setup | No | The paper is theoretical and outlines a framework for algorithmic solutions; it does not detail specific experimental setups, hyperparameters, or system-level training settings. |