Nash Equilibria in Concurrent Games with Lexicographic Preferences
Authors: Julian Gutierrez, Aniello Murano, Giuseppe Perelli, Sasha Rubin, Michael Wooldridge
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study concurrent games with finite-memory strategies where players are given a B uchi and a mean-payoff objective, which are related by a lexicographic order: a player first prefers to satisfy its B uchi objective, and then prefers to minimise costs, which are given by a mean-payoff function. In particular, we show that deciding the existence of a strict Nash equilibrium in such games is decidable, even if players deviations are implemented as infinite memory strategies. |
| Researcher Affiliation | Academia | Julian Gutierrez1, Aniello Murano2, Giuseppe Perelli1, Sasha Rubin2, Michael Wooldridge1 1University of Oxford 2Universit a degli Studi di Napoli Federico II |
| Pseudocode | No | The paper describes algorithms and mathematical constructions, such as the linear program (LP) in Section 3.3, but it does not present any formal pseudocode blocks or algorithms labeled as such. |
| Open Source Code | No | The paper does not contain any statement about making source code for their methodology publicly available, nor does it provide any links to a code repository. |
| Open Datasets | No | The paper is theoretical and does not use or evaluate on any datasets, therefore no dataset access information for training is provided. |
| Dataset Splits | No | The paper is theoretical and does not conduct empirical experiments, therefore no training, validation, or test data splits are mentioned. |
| Hardware Specification | No | The paper is theoretical and does not describe any empirical experiments, therefore no hardware specifications for running experiments are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe any empirical experiments that would require specific software dependencies with version numbers. While it mentions linear programming, it does not specify a particular solver or its version. |
| Experiment Setup | No | The paper is theoretical and does not describe any empirical experiments, therefore no experimental setup details such as hyperparameters or training configurations are provided. |