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    Rights statement: © ACM, 2020. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in AAMAS '20: Proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems, 2020 https://dl.acm.org/doi/10.5555/3398761.3399054

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On-line Estimators for Ad-hoc Task Allocation: Extended Abstract

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

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On-line Estimators for Ad-hoc Task Allocation: Extended Abstract. / Shafipour Yourdshahi, Elnaz; Do Carmo Alves, Matheus; Soriano Marcolino, Leandro et al.
Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2020). ACM, 2020. p. 1999–2001.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Shafipour Yourdshahi, E, Do Carmo Alves, M, Soriano Marcolino, L & Angelov, P 2020, On-line Estimators for Ad-hoc Task Allocation: Extended Abstract. in Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2020). ACM, pp. 1999–2001. <https://dl.acm.org/doi/10.5555/3398761.3399054>

APA

Shafipour Yourdshahi, E., Do Carmo Alves, M., Soriano Marcolino, L., & Angelov, P. (2020). On-line Estimators for Ad-hoc Task Allocation: Extended Abstract. In Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2020) (pp. 1999–2001). ACM. https://dl.acm.org/doi/10.5555/3398761.3399054

Vancouver

Shafipour Yourdshahi E, Do Carmo Alves M, Soriano Marcolino L, Angelov P. On-line Estimators for Ad-hoc Task Allocation: Extended Abstract. In Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2020). ACM. 2020. p. 1999–2001

Author

Shafipour Yourdshahi, Elnaz ; Do Carmo Alves, Matheus ; Soriano Marcolino, Leandro et al. / On-line Estimators for Ad-hoc Task Allocation : Extended Abstract. Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2020). ACM, 2020. pp. 1999–2001

Bibtex

@inproceedings{7d69ee44a53d46b6901e6699b99a53f5,
title = "On-line Estimators for Ad-hoc Task Allocation: Extended Abstract",
abstract = "It is essential for agents to work together with others to accomplish common missions without previous knowledge of the team-mates, a challenge known as ad-hoc teamwork. In these systems, an agent estimates the algorithm and parameters of others in an on-line manner, in order to decide its own actions for effective teamwork. Meanwhile, agents often must coordinate in a decentralised fashion to complete tasks that are displaced in an environment (e.g., in foraging, demining, rescue or fire control), where each member autonomously chooses which task to perform. By harnessing this knowledge, better estimation techniques would lead to better performance. Hence, we present On-line Estimators for Ad-hoc Task Allocation, a novel algorithm for team-mates' type and parameter estimation in decentralised task allocation. We ran experiments in the level-based foraging domain, where we obtain lower error in parameter and type estimation than previous approaches, and a significantly better performance in finishing all tasks.",
author = "{Shafipour Yourdshahi}, Elnaz and {Do Carmo Alves}, Matheus and {Soriano Marcolino}, Leandro and Plamen Angelov",
note = "{\textcopyright} ACM, 2020. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in AAMAS '20: Proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems, 2020 https://dl.acm.org/doi/10.5555/3398761.3399054",
year = "2020",
month = may,
day = "9",
language = "English",
isbn = "9781450375184",
pages = "1999–2001",
booktitle = "Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2020)",
publisher = "ACM",

}

RIS

TY - GEN

T1 - On-line Estimators for Ad-hoc Task Allocation

T2 - Extended Abstract

AU - Shafipour Yourdshahi, Elnaz

AU - Do Carmo Alves, Matheus

AU - Soriano Marcolino, Leandro

AU - Angelov, Plamen

N1 - © ACM, 2020. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in AAMAS '20: Proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems, 2020 https://dl.acm.org/doi/10.5555/3398761.3399054

PY - 2020/5/9

Y1 - 2020/5/9

N2 - It is essential for agents to work together with others to accomplish common missions without previous knowledge of the team-mates, a challenge known as ad-hoc teamwork. In these systems, an agent estimates the algorithm and parameters of others in an on-line manner, in order to decide its own actions for effective teamwork. Meanwhile, agents often must coordinate in a decentralised fashion to complete tasks that are displaced in an environment (e.g., in foraging, demining, rescue or fire control), where each member autonomously chooses which task to perform. By harnessing this knowledge, better estimation techniques would lead to better performance. Hence, we present On-line Estimators for Ad-hoc Task Allocation, a novel algorithm for team-mates' type and parameter estimation in decentralised task allocation. We ran experiments in the level-based foraging domain, where we obtain lower error in parameter and type estimation than previous approaches, and a significantly better performance in finishing all tasks.

AB - It is essential for agents to work together with others to accomplish common missions without previous knowledge of the team-mates, a challenge known as ad-hoc teamwork. In these systems, an agent estimates the algorithm and parameters of others in an on-line manner, in order to decide its own actions for effective teamwork. Meanwhile, agents often must coordinate in a decentralised fashion to complete tasks that are displaced in an environment (e.g., in foraging, demining, rescue or fire control), where each member autonomously chooses which task to perform. By harnessing this knowledge, better estimation techniques would lead to better performance. Hence, we present On-line Estimators for Ad-hoc Task Allocation, a novel algorithm for team-mates' type and parameter estimation in decentralised task allocation. We ran experiments in the level-based foraging domain, where we obtain lower error in parameter and type estimation than previous approaches, and a significantly better performance in finishing all tasks.

M3 - Conference contribution/Paper

SN - 9781450375184

SP - 1999

EP - 2001

BT - Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2020)

PB - ACM

ER -