Unknown Agents in Friends Oriented Hedonic Games: Stability and Complexity
Authors: Nathanaël Barrot, Kazunori Ota, Yuko Sakurai, Makoto Yokoo1756-1763
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study hedonic games under friends appreciation, where each agent considers other agents friends, enemies, or unknown agents. Although existing work assumed that unknown agents have no impact on an agent s preference, it may be that her preference depends on the number of unknown agents in her coalition. We extend the existing preference, friends appreciation, by proposing two alternative attitudes toward unknown agents, extraversion and introversion, depending on whether unknown agents have a slightly positive or negative impact on preference. When each agent prefers coalitions with more unknown agents, we show that both core stable outcomes and individually stable outcomes may not exist. We also prove that deciding the existence of the core and the existence of an individual stable coalition structure are respectively NPNP-complete and NP-complete. |
| Researcher Affiliation | Academia | Nathanael Barrot,1,2 Kazunori Ota,2 Yuko Sakurai,3 Makoto Yokoo2,1 1RIKEN, Center for Advanced Intelligence Project (AIP), 2Kyushu University, 3National Institute of Advanced Industrial Science and Technology (AIST) nathanaelbarrot@gmail.com, ota@agent.inf.kyushu-u.ac.jp yuko.sakurai@aist.go.jp, yokoo@inf.kyushu-u.ac.jp |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not include any statements about releasing source code or provide links to a code repository. |
| Open Datasets | No | The paper is theoretical and does not use datasets for training or evaluation. Therefore, there is no mention of publicly available datasets or access information. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with dataset splits. No information on training, validation, or test splits is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe any computational experiments that would require specifying hardware used. |
| Software Dependencies | No | The paper is theoretical and does not mention any software dependencies with specific version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or system-level training settings. |