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Meir Kalech and Gal A. Kaminka. Coordination Diagnostic Algorithms for Teams of Situated Agents: Scaling-Up. Computational Intelligence, 27(3):393–421, 2011.
Agents in a team should be in agreement. Unfortunately, they may come to disagree due to sensor uncertainty, intermittent communication failures, etc. Once a disagreement occurs the agents should detect and diagnose the disagreement. Current diagnostic techniques do not scale well with the number of agents, as they have high communication and computation complexity. We present novel techniques that enable scalability in three ways. First, we use communications early in the diagnostic process, to stave off unneeded reasoning, which ultimately leads to unneeded communications. Second, we use light-weight (and inaccurate) behavior recognition to focus the diagnostic reasoning on beliefs of agents that might be in conflict. Finally, we propose diagnosing only to a limited number of representative agents (instead of all the agents). We examine these techniques in large-scale teams of situated agents in two domains, and show that combining the techniques produces a diagnostic process which is highly scalable in both communication and computation.
The article's official web page is at: http://onlinelibrary.wiley.com/doi/10.1111/j.1467-8640.2011.00386.x/abstract.
@Article{ci11,
author = {Meir Kalech and Gal A. Kaminka},
title = {Coordination Diagnostic Algorithms for Teams of Situated Agents: Scaling-Up},
journal = {Computational Intelligence},
year = {2011},
OPTkey = {},
volume = {27},
number = {3},
pages = {393--421},
OPTmonth = {},
OPTnote = {},
abstract = {Agents in a team should be in agreement. Unfortunately, they may come
to disagree due to sensor uncertainty, intermittent communication
failures, etc. Once a disagreement occurs the agents should detect and diagnose
the disagreement. Current diagnostic techniques do
not scale well with the number of agents, as they have high
communication and computation complexity. We present novel
techniques that enable scalability in three ways. First, we use
communications early in the diagnostic process, to stave off unneeded
reasoning, which ultimately leads to unneeded communications.
Second, we use light-weight (and inaccurate) behavior recognition to
focus the diagnostic reasoning on beliefs of agents that might be in
conflict. Finally, we propose diagnosing only to a limited number of
representative agents (instead of all the agents).
We examine these techniques in large-scale teams of situated agents
in two domains, and show that combining the techniques produces a
diagnostic process which is highly scalable in both communication and computation.},
OPTannote = {}
}
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