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Gal A. Kaminka and Michael Bowling. Towards Robust Teams with Many Agents.
Technical Report CMU-CS-01-159, Carnegie Mellon University, 2001.
[PDF]233.3kB [gzipped postscript]84.4kB
Agents in deployed multi-agent systems monitor other agents to coordinate and collaborate robustly. However, as the number of agents monitored is scaled up, two key challenges arise: (i) the number of monitoring hypotheses to be considered can grow exponentially in the number of agents; and (ii) agents become physically and logically unconnected (unobservable) to their peers. This paper examines these challenges in teams of cooperating agents, focusing on a monitoring task that is of particular importance to robust teamwork: detecting disagreements among team-members. We present YOYO, a highly scalable disagreement-detection algorithm which guarantees sound detection in time linear in the number of agents despite the exponential number of hypotheses. In addition, we present new upper bounds about the number of agents that must be monitored in a team to guarantee disagreement detection. Both YOYO and the new bounds are explored analytically and empirically in thousands of monitoring problems, scaled to thousands of agents.
@techreport{scaleup-techreport,
author = {Gal A. Kaminka and Michael Bowling},
title = {Towards Robust Teams with Many Agents},
institution = {Carnegie Mellon University},
year = {2001},
key = {Kaminka and Bowling},
number = {CMU-CS-01-159},
abstract = {Agents in deployed multi-agent systems monitor other agents to
coordinate and collaborate robustly. However, as the number of agents
monitored is scaled up, two key challenges arise: (i) the number of
monitoring hypotheses to be considered can grow exponentially in the
number of agents; and (ii) agents become physically and logically
unconnected (unobservable) to their peers. This paper examines these
challenges in teams of cooperating agents, focusing on a monitoring
task that is of particular importance to robust teamwork: detecting
disagreements among team-members. We present YOYO, a highly scalable
disagreement-detection algorithm which guarantees sound detection in
time linear in the number of agents despite the exponential number of
hypotheses. In addition, we present new upper bounds about the number
of agents that must be monitored in a team to guarantee disagreement
detection. Both YOYO and the new bounds are explored analytically and
empirically in thousands of monitoring problems, scaled to thousands
of agents.},
wwwnote = {},
}
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