Applying Constraint Reasoning to Real-world Distributed Task Allocation

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.