Learning the Value of Teamwork to Form Efficient Teams
Abstract
In this paper we describe a novel approach to team formation based on the value of inter-agent interactions. Specifically, we propose a model of teamwork that considers outcomes from chains of interactions between agents. Based on our model, we devise a number of network metrics to capture the contribution of interactions between agents. This is then used to learn the value of teamwork from historical team performance data. We apply our model to predict team performance and validate our approach using real-world team performance data from the 2018 FIFA World Cup. Our model is shown to better predict the real-world performance of teams by up to 46% compared to models that ignore inter-agent interactions.
Cite
Text
Beal et al. "Learning the Value of Teamwork to Form Efficient Teams." AAAI Conference on Artificial Intelligence, 2020. doi:10.1609/AAAI.V34I05.6192Markdown
[Beal et al. "Learning the Value of Teamwork to Form Efficient Teams." AAAI Conference on Artificial Intelligence, 2020.](https://mlanthology.org/aaai/2020/beal2020aaai-learning/) doi:10.1609/AAAI.V34I05.6192BibTeX
@inproceedings{beal2020aaai-learning,
title = {{Learning the Value of Teamwork to Form Efficient Teams}},
author = {Beal, Ryan and Changder, Narayan and Norman, Timothy J. and Ramchurn, Sarvapali D.},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2020},
pages = {7063-7070},
doi = {10.1609/AAAI.V34I05.6192},
url = {https://mlanthology.org/aaai/2020/beal2020aaai-learning/}
}