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

Shafipour Yourdshahi, Elnaz and Do Carmo Alves, Matheus and Soriano Marcolino, Leandro and Angelov, Plamen (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, 1999–2001. ISBN 9781450375184

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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.

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Contribution in Book/Report/Proceedings
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© 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
ID Code:
142120
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Deposited On:
10 Mar 2020 10:05
Refereed?:
Yes
Published?:
Published
Last Modified:
04 Dec 2020 07:23