Using Swarm-GAP for Distributed Task Allocation in Complex Scenarios
This paper addresses distributed task allocation in complex scenarios modeled using the distributed constraint optimization problem (DCOP) formalism. It is well known that DCOP, when used to model complex scenarios, generates problems with exponentially growing number of parameters. However, those scenarios are becoming ubiquitous in real-world applications. Therefore, approximate solutions are necessary. We propose and evaluate an algorithm for distributed task allocation. This algorithm, called Swarm-GAP, is based on theoretical models of division of labor in social insect colonies. It uses a probabilistic decision model. Swarm-GAP is experimented both in a scenario from RoboCup Rescue and an abstract simulation environment. We show that Swarm-GAP achieves similar results as other recent proposed algorithm with a reduction in communication and computation. Thus, our approach is highly scalable regarding both the number of agents and tasks.
KeywordsMultiagent System Task Allocation Complex Scenario Average Reward Total Reward
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