Bidding Graph Games with Partially-Observable Budgets
Authors: Guy Avni, Ismael Jecker, Đorđe Žikelić
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We show that while for some bidding mechanisms and objectives, it is straightforward to adapt the results from the full-information setting to the partialinformation setting, for others, the analysis is significantly more challenging, requires new techniques, and gives rise to interesting results. Specifically, we study games with meanpayoff objectives in combination with poorman bidding. We construct optimal strategies for a partially-informed player who plays against a fully-informed adversary. We show that, somewhat surprisingly, the value under pure strategies does not necessarily exist in such games. |
| Researcher Affiliation | Academia | Guy Avni1, Ismäel Jecker2, Ðor de Žikeli c3 1University of Haifa 2University of Warsaw 3Institute of Science and Technology Austria (ISTA) |
| Pseudocode | No | The paper is theoretical and does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link regarding the release of open-source code for the described methodology. |
| Open Datasets | No | The paper describes theoretical game models and does not use or refer to any publicly available or open datasets for empirical evaluation. |
| Dataset Splits | No | The paper does not present empirical experiments, and thus does not specify training, validation, or test dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe empirical experiments, therefore no hardware specifications are mentioned for running experiments. |
| Software Dependencies | No | The paper is theoretical and does not describe empirical experiments, therefore no specific software dependencies with version numbers are provided. |
| Experiment Setup | No | The paper does not describe empirical experiments or provide details about an experimental setup, such as hyperparameters or training settings. |