TY - GEN

T1 - On-Line Estimators for Ad-Hoc Task Allocation

AU - Shafipour Yourdshahi, Elnaz

AU - Aparecido do Carmo Alves, Matheus

AU - Soriano Marcolino, Leandro

AU - Angelov, Plamen

PY - 2020

Y1 - 2020

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.

KW - ad-hoc teamwork

KW - coordination and collaboration

KW - on-line learning

M3 - Conference contribution/Paper

SN - 9781450375184

T3 - Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS

SP - 1999

EP - 2001

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

PB - International Foundation for Autonomous Agents and Multiagent Systems

CY - Richland, SC

ER -