A Distributed Anytime Algorithm for Dynamic Task Allocation in Multi-Agent Systems

Authors

  • Kathryn Macarthur University of Southampton
  • Ruben Stranders University of Southampton
  • Sarvapali Ramchurn University of Southampton
  • Nicholas Jennings University of Southampton

Abstract

We introduce a novel distributed algorithm for multi-agent task allocation problems where the sets of tasks and agents constantly change over time. We build on an existing anytime algorithm (fast-max-sum), and give it significant new capa- bilities: namely, an online pruning procedure that simplifies the problem, and a branch-and-bound technique that reduces the search space. This allows us to scale to problems with hundreds of tasks and agents. We empirically evaluate our algorithm against established benchmarks and find that, even in such large environments, a solution is found up to 31% faster, and with up to 23% more utility, than state-of-the-art approximation algorithms. In addition, our algorithm sends up to 30% fewer messages than current approaches when the set of agents or tasks changes.

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Published

2011-08-04

How to Cite

Macarthur, K., Stranders, R., Ramchurn, S., & Jennings, N. (2011). A Distributed Anytime Algorithm for Dynamic Task Allocation in Multi-Agent Systems. Proceedings of the AAAI Conference on Artificial Intelligence, 25(1), 701-706. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/7866

Issue

Section

AAAI Technical Track: Multiagent Systems