Multi-Agent Systems with Quantitative Satisficing Goals
Authors: Senthil Rajasekaran, Suguman Bansal, Moshe Y. Vardi
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The paper focuses on "automata-based algorithms to find pure-strategy Nash equilibria" and showing "these algorithms extend to scenarios in which agents have multiple thresholds". It also discusses "complexity-theoretic benefit" and "PSPACE upper bound". It lacks any mention of empirical evaluation, datasets, or performance metrics from experiments. |
| Researcher Affiliation | Academia | Senthil Rajasekaran1 , Suguman Bansal2 and Moshe Y. Vardi1 1Rice University 2Georgia Institute of Technology sr79@rice.edu, suguman@gatech.edu, vardi@rice.edu |
| Pseudocode | No | The paper describes theoretical constructions and algorithms in prose and mathematical notation but does not include any blocks labeled as "Pseudocode" or "Algorithm". |
| Open Source Code | No | The paper does not mention providing open-source code for the methodology it describes. |
| Open Datasets | No | The paper is theoretical and does not discuss datasets for training or any experimental data. |
| Dataset Splits | No | The paper is theoretical and does not discuss dataset splits for validation or any experimental data. |
| Hardware Specification | No | This is a theoretical paper. It does not describe any hardware used for running experiments. |
| Software Dependencies | No | This is a theoretical paper. It does not list any specific software dependencies with version numbers for experimental reproducibility. |
| Experiment Setup | No | This is a theoretical paper. It does not describe any experimental setup details such as hyperparameters or training configurations. |