Multi-Agent Team Formation: Solving Complex Problems by Aggregating Opinions
Abstract
It is known that we can aggregate the opinions of different agents to find high-quality solutions to complex problems. However, choosing agents to form a team is still a great challenge. Moreover, it is essential to use a good aggregation methodology in order to unleash the potential of a given team in solving complex problems. In my thesis, I present two different novel models to aid in the team formation process. Moreover, I propose a new methodology for extracting rankings from existing agents. I show experimental results both in the Computer Go domain and in the building design domain.
Cite
Text
Marcolino. "Multi-Agent Team Formation: Solving Complex Problems by Aggregating Opinions." AAAI Conference on Artificial Intelligence, 2015. doi:10.1609/AAAI.V29I1.9257Markdown
[Marcolino. "Multi-Agent Team Formation: Solving Complex Problems by Aggregating Opinions." AAAI Conference on Artificial Intelligence, 2015.](https://mlanthology.org/aaai/2015/marcolino2015aaai-multi/) doi:10.1609/AAAI.V29I1.9257BibTeX
@inproceedings{marcolino2015aaai-multi,
title = {{Multi-Agent Team Formation: Solving Complex Problems by Aggregating Opinions}},
author = {Marcolino, Leandro Soriano},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2015},
pages = {4257-4258},
doi = {10.1609/AAAI.V29I1.9257},
url = {https://mlanthology.org/aaai/2015/marcolino2015aaai-multi/}
}