Over the past few years, emergent computing based techniques such as swarming have evolved as an attractive technique to design coordination protocols in large-scale distributed systems and massively multi-agent systems. In this paper, we consider a search-and-execute problem domain where agents have to discover tasks online and perform them in a distributed, collaborative manner. We specifically focus on the problem of distributed coordination between agents to dynamically allocate the tasks among themselves. To address this problem, we describe a novel technique that combines a market-based dynamic pricing algorithm to control the task priorities with a swarming-based coordination technique to disseminate task information across the agents. Experimental results within a simulated environment for a distributed aided target recognition application show that the dynamic pricing based task selection strategies compare favorably with other heuristic-based task selection strategies in terms of task completion times while achieving a significant reduction in communication overhead. © 2008 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Dasgupta, P., & Hoeing, M. (2008). Dynamic pricing algorithms for task allocation in multi-agent swarms. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5043 LNAI, pp. 64–79). https://doi.org/10.1007/978-3-540-85449-4_5
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