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Authors: Amar Nath ; Arun Ar and Rajdeep Niyogi

Affiliation: IIT Roorkee, India 247667, India

ISBN: 978-989-758-395-7

ISSN: 2184-433X

Keyword(s): Multi-team Formation, Distributed Algorithm, Dynamic Environment, Multi-agent Coordination and Cooperation.

Abstract: Multi-team formation has received considerable attention in the last decade. Most existing approaches for team formation assume complete knowledge of the environment, and the problem is reduced to a combinatorial optimization problem. In a dynamic environmnt, the agents may be spatially distributed, have limited information, lack global knowledge, and update their knowledge by communicating with each other. In such a setting, it would be more appropriate for the agents to form teams by themselves in a distributed manner. In this paper, we suggest a distributed algorithm for multi-team formation (DMTF). The implementation of a distributed algorithm is quite challenging, and traditionally such algorithms are presented in a theoretical way. We implemented the proposed algorithm using a multi-robot simulator. The experimental results show the efficacy of the approach.

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Paper citation in several formats:
Nath, A.; Ar, A. and Niyogi, R. (2020). DMTF: A Distributed Algorithm for Multi-team Formation.In Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-758-395-7, ISSN 2184-433X, pages 152-160. DOI: 10.5220/0008914701520160

@conference{icaart20,
author={Amar Nath. and Arun Ar. and Rajdeep Niyogi.},
title={DMTF: A Distributed Algorithm for Multi-team Formation},
booktitle={Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2020},
pages={152-160},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008914701520160},
isbn={978-989-758-395-7},
}

TY - CONF

JO - Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - DMTF: A Distributed Algorithm for Multi-team Formation
SN - 978-989-758-395-7
AU - Nath, A.
AU - Ar, A.
AU - Niyogi, R.
PY - 2020
SP - 152
EP - 160
DO - 10.5220/0008914701520160

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