@conference {1510352,
	title = {Applying Constraint Reasoning to Real-world Distributed Task Allocation },
	booktitle = {Autonomous Agents and Multi-Agent Systems Workshop on Distributed Constraint Reasoning},
	year = {2002},
	abstract = {Distributed task allocation algorithms requires a set of agents
to intelligently allocate their resources to a set of tasks. The
problem is often complicated by the fact that resources may
be limited, the set of tasks may not be exactly known, and
the set of tasks may change over time. Previous resource allocation algorithms have not been able to handle over- constrained situations, the uncertainty in the environment
and/or dynamics. In this paper, we present extensions to
an algorithm for distributed constraint optimization, called
Adopt-SC which allows it to be applied in such real-world
domains. The approach relies on maintaining a probability distribution over tasks that are potentially present. The
distribution is updated with both information from local sensors and information inferred from communication between agents. We present promising results with the approach on a distributed task allocation problem consisting
of a set of stationary sensors that must track a moving target. The techniques proposed in this paper are evaluated on
real hardware tracking real moving targets.},
	author = {Paul Scerri and Pragnesh Jay Modi and Wei-Min Shen and Tambe, Milind}
}
