We present a distributed algorithm for task allocation in multiagent systems for settings in which agents and tasks are geographically dispersed in two-dimensional space. We describe a method that enables agents to determine individually how to move so that they are, as a group, efficiently assigned to tasks. The method comprises two algorithms and is especially useful in environments with very large numbers of agent and task nodes. One algorithm adapts computational geometry techniques to determine adjacency information for the agent nodes given the geographical positions of agents and tasks. This adjacency information is used to determine the visible nodes that are most relevant to an agent's decision making process and to eliminate those that it should not consider. The second algorithm uses local heuristics based solely on an agent's adjacent nodes to determine its course of action. This method yields improved task allocations compared to previous algorithms proposed for similar environments. We also present a modification to the second algorithm that improves performance in environments in which multiple agents are required to complete a single task.
The work of Sander et al. REF introduces computational geometry-based algorithms for distributed task allocation in geographical domains.
A scalable, distributed algorithm for efficient task allocation
AAMAS '02
Computer Science