Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates.
Multi-Agent Team Formation: Solving Complex Problems by Aggregating Opinions
Authors: Leandro Soriano Marcolino
AAAI 2015 | Venue PDF | LLM Run Details | Input Tokens: 8,758 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,169 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | All my predictions are veriο¬ed in a real system of voting agents, in the Computer Go domain. I also performed an experimental study in the building design domain. I summarize here some of the main experimental results obtained so far, in the Computer Go domain and in the building design domain. |
| Researcher Affiliation | Academia | Leandro Soriano Marcolino University of Southern California, Los Angeles, CA, 90089, USA EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described. |
| Open Datasets | No | The paper mentions experiments in the "Computer Go domain" and "building design domain" and lists Computer Go playing agents (Fuego, Gnugo, Pachi, Mogo). However, it does not provide concrete access information (link, DOI, specific citation with authors/year) for a publicly available or open dataset used for training or evaluation. The names of the agents are given, but not a dataset of games or building designs. |
| Dataset Splits | No | The paper does not provide specific dataset split information for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers. It mentions Computer Go playing agents (Fuego, Gnugo, Pachi, Mogo), but these are not listed with versions as software dependencies for reproduction. |
| Experiment Setup | No | The paper does not contain specific experimental setup details such as hyperparameter values, training configurations, or system-level settings. |