Generalized Discrete Preference Games
Authors: Vincenzo Auletta, Ioannis Caragiannis, Diodato Ferraioli, Clemente Galdi, Giuseppe Persiano
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we define and study the novel class of generalized discrete preference games. These games have additional characteristics that can model social relations to allies or competitors and complex relations among more than two agents. Moreover, they introduce different levels of strength for each relation, and they personalize the dependence of each agent to her neighborhood. We show that these novel games admit generalized ordinal potential functions and, more importantly, that every two-strategy game that admits a generalized ordinal potential function is structurally equivalent to a generalized discrete preference game. |
| Researcher Affiliation | Academia | Vincenzo Auletta University of Salerno auletta@unisa.it Ioannis Caragiannis University of Patras caragian@upatras.gr Diodato Ferraioli University of Salerno dferraioli@unisa.it Clemente Galdi University of Naples Federico II clemente.galdi@unina.it Giuseppe Persiano University of Salerno giuper@unisa.it |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not use datasets for experiments. |
| Dataset Splits | No | The paper is theoretical and does not involve data partitioning or experiments, so no dataset split information is provided. |
| Hardware Specification | No | The paper is theoretical and does not mention any specific hardware used for running experiments. |
| 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 any experimental setup details such as hyperparameters or training configurations. |