Improving Computational Efficiency in Crowded Task Allocation Games with Coupled Constraints

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Multi-agent task allocation is a well-studied field with many proven algorithms. In real-world applications, many tasks have complicated coupled relationships that affect the feasibility of some algorithms. In this paper, we leverage on the properties of potential games and introduce a scheduling algorithm to provide feasible solutions in allocation scenarios with complicated spatial and temporal dependence. Additionally, we propose the use of random sampling in a Distributed Stochastic Algorithm to enhance speed of convergence. We demonstrate the feasibility of such an approach in a simulated disaster relief operation and show that feasibly good results can be obtained when the confirmation and sample size requirements are properly selected.
Publisher
MDPI
Issue Date
2019-05
Language
English
Article Type
Article
Citation

APPLIED SCIENCES-BASEL, v.9, no.10

ISSN
2076-3417
DOI
10.3390/app9102117
URI
http://hdl.handle.net/10203/263757
Appears in Collection
AE-Journal Papers(저널논문)
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