Scalable Agent Modeling for Large Multiagent Systems
Authors: Carrie Rebhuhn
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The performance shown in Figure 1 indicates that the inclusion of stereotypes is more effective than the difference utility at noise reduction in some cases. |
| Researcher Affiliation | Academia | Carrie Rebhuhn Oregon State University rebhuhnc@onid.orst.edu |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | The full results and details for the UAS domain are available in the workshop paper that appeared in ALA 2014 (Rebhuhn, Knudson, and Tumer 2014). |
| Dataset Splits | No | The paper mentions using the UAS domain but does not provide specific dataset split information (percentages, sample counts, or detailed splitting methodology). |
| 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 with version numbers. |
| Experiment Setup | No | The paper mentions the UAS domain and a global utility function but does not provide specific experimental setup details such as hyperparameters or training configurations within the main text, instead referring to an external paper for 'full results and details'. |