Quantifying the impact of communication on performance in multi-agent teams

Quantifying the impact of communication on performance in multi-agent teams In this work, we relate the extent and quality of inter-agent communication and the overall performance in teams of multiple agents. Specifically, we examine the RoboCup Soccer Simulation 2D League, and carry out multiple simulation experiments against two evenly matched teams. For each simulated run (a 2D soccer simulation game), we generate the communication efficiencies (i.e., communications sent/communications received) for each agent pair. Applying linear regression and principal component analyses, we then correlate these efficiencies with measures of performance (i.e., goals scored and goals conceded), enabling the construction of inter-agent communication networks. Analysis of these networks highlights the microscopic player-to-player and macroscopic role-to-role communications correlated with performance. The approach determines the salient pathways within inter-agent communications which globally affect the coordination and the overall performance in multi-agent teams. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Artificial Life and Robotics Springer Journals

Quantifying the impact of communication on performance in multi-agent teams

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Publisher
Springer Japan
Copyright
Copyright © 2017 by Her Majesty the Queen in Right of Australia
Subject
Computer Science; Artificial Intelligence (incl. Robotics); Computation by Abstract Devices; Control, Robotics, Mechatronics
ISSN
1433-5298
eISSN
1614-7456
D.O.I.
10.1007/s10015-017-0367-0
Publisher site
See Article on Publisher Site

Abstract

In this work, we relate the extent and quality of inter-agent communication and the overall performance in teams of multiple agents. Specifically, we examine the RoboCup Soccer Simulation 2D League, and carry out multiple simulation experiments against two evenly matched teams. For each simulated run (a 2D soccer simulation game), we generate the communication efficiencies (i.e., communications sent/communications received) for each agent pair. Applying linear regression and principal component analyses, we then correlate these efficiencies with measures of performance (i.e., goals scored and goals conceded), enabling the construction of inter-agent communication networks. Analysis of these networks highlights the microscopic player-to-player and macroscopic role-to-role communications correlated with performance. The approach determines the salient pathways within inter-agent communications which globally affect the coordination and the overall performance in multi-agent teams.

Journal

Artificial Life and RoboticsSpringer Journals

Published: Jun 1, 2017

References

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