Evaluating the Robustness of Game Theoretic Solutions When Using Abstraction
Authors: Oscar Veliz
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present an initial empirical study of the robustness of several solution methods when using abstracted games. and We conducted many experiments to identify the best parameter settings for QRE, CH, and QLK agents and then ran round robin tournaments on over 300 games, 18 unique agents, and 4 levels of abstractions (including no abstraction). |
| Researcher Affiliation | Academia | Oscar Veliz University of Texas at El Paso 500 W. University Ave, El Paso, TX, 79968, USA (915) 747-6373 osveliz@miners.utep.edu |
| Pseudocode | No | The paper describes methods like Top N and KMeans but does not present them in pseudocode or algorithm blocks. |
| Open Source Code | No | The paper includes a footnote '1http://www.cs.utep.edu/kiekintveld/students/veliz/index.html' but does not explicitly state that this link provides the source code for the methodology described in the paper. |
| Open Datasets | No | The paper mentions running experiments 'on over 300 games' but does not provide any concrete access information (link, DOI, repository, or citation) for a publicly available or open dataset. |
| Dataset Splits | No | The paper does not provide specific dataset split information (percentages, sample counts, or detailed splitting methodology) for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | No | The paper mentions 'identify the best parameter settings for QRE, CH, and QLK agents' but does not provide these specific parameter values or other concrete experimental setup details (like learning rates, batch sizes, or optimizer settings) in the main text. |