Nash Equilibria in Multi-Agent Swarms

Carsten Hahn, Thomy Phan, Sebastian Feld, Christoph Roch, Fabian Ritz, Andreas Sedlmeier, Thomas Gabor, Claudia Linnhoff-Popien

2020

Abstract

In various settings in nature or robotics, swarms offer various benefits as a structure that can be joined easily and locally but still offers more resilience or efficiency at performing certain tasks. When these benefits are rewarded accordingly, even purely self-interested Multi-Agent reinforcement learning systems will thus learn to form swarms for each individual’s benefit. In this work we show, however, that under certain conditions swarms also pose Nash equilibria when interpreting the agents’ given task as multi-player game. We show that these conditions can be achieved by altering the area size (while allowing individual action choices) in a setting known from literature. We conclude that aside from offering valuable benefits to rational agents, swarms may also form due to pressuring deviants from swarming behavior into joining the swarm as is typical for Nash equilibria in social dilemmas.

Paper Citation

in Harvard Style

Hahn C., Phan T., Feld S., Roch C., Ritz F., Sedlmeier A., Gabor T. and Linnhoff-Popien C. (2020). Nash Equilibria in Multi-Agent Swarms. In Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-758-395-7, pages 234-241. DOI: 10.5220/0008990802340241

in Bibtex Style

@conference{icaart20,
author={Carsten Hahn and Thomy Phan and Sebastian Feld and Christoph Roch and Fabian Ritz and Andreas Sedlmeier and Thomas Gabor and Claudia Linnhoff-Popien},
title={Nash Equilibria in Multi-Agent Swarms},
booktitle={Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2020},
pages={234-241},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008990802340241},
isbn={978-989-758-395-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - Nash Equilibria in Multi-Agent Swarms
SN - 978-989-758-395-7
AU - Hahn C.
AU - Phan T.
AU - Feld S.
AU - Roch C.
AU - Ritz F.
AU - Sedlmeier A.
AU - Gabor T.
AU - Linnhoff-Popien C.
PY - 2020
SP - 234
EP - 241
DO - 10.5220/0008990802340241