@conference {1509600,
	title = {Allocating tasks in extreme teams },
	booktitle = {Fourth International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS)},
	year = {2005},
	abstract = {Extreme teams, large-scale agent teams operating in dynamic environments, are on the horizon. Such environments are problematic
for current task allocation algorithms due to the lack of locality in
agent interactions. We propose a novel distributed task allocation
algorithm for extreme teams, called LA-DCOP, that incorporates
three key ideas. First, LA-DCOP{\textquoteright}s task allocation is based on a
dynamically computed minimum capability threshold which uses
approximate knowledge of overall task load. Second, LA-DCOP
uses tokens to represent tasks and further minimize communication. Third, it creates potential tokens to deal with inter-task constraints of simultaneous execution. We show that LA-DCOP convincingly outperforms competing distributed task allocation algorithms while using orders of magnitude fewer messages, allowing a
dramatic scale-up in extreme teams, upto a fully distributed, proxybased team of 200 agents. Varying threshold are seen as a key to
outperforming competing distributed algorithms in the domain of
simulated disaster rescue.},
	author = {Paul Scerri and A. Farinelli and Steven Okamoto and Tambe, Milind}
}
