Gal A. Kaminka's Publications

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Coordination Diagnostic Algorithms for Teams of Situated Agents: Scaling-Up

Meir Kalech and Gal A. Kaminka. Coordination Diagnostic Algorithms for Teams of Situated Agents: Scaling-Up. Computational Intelligence, 27(3):393–421, 2011.

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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.

Additional Information

The article's official web page is at: http://onlinelibrary.wiley.com/doi/10.1111/j.1467-8640.2011.00386.x/abstract.

BibTeX

@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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