Synthesis of Controllable Nash Equilibria in Quantitative Objective Game
Authors: Shaull Almagor, Orna Kupferman, Giuseppe Perelli
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our algorithms make use of strategy logic and decision procedures for it. Thus, we are able to handle the richer quantitative setting using existing tools. In particular, we show that the cooperative and non-cooperative versions of LTL[F] rational synthesis are 2EXPTIME-complete and in 3EXPTIME, respectively, and that so are the problems of calculating the various stability-inefficiency measures, and other measures that quantify the game and its outcomes. |
| Researcher Affiliation | Academia | Shaull Almagor1, Orna Kupferman2, Giuseppe Perelli1 , 1 University of Oxford 2 Hebrew University |
| Pseudocode | No | The paper presents logical formulas and theorems but does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statements about releasing code or links to source code repositories. |
| Open Datasets | No | The paper is theoretical and does not use datasets for training. Therefore, no information about dataset availability for training is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve experimental data. Therefore, no dataset split information for validation is provided. |
| Hardware Specification | No | The paper is theoretical and does not discuss experimental setups or hardware used. |
| Software Dependencies | No | The paper mentions 'Strategy Logic' and 'LTL[F]' as formalisms and tools, but does not specify any software names with version numbers required for replication. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup, hyperparameters, or training configurations. |