Journal
APPLIED INTELLIGENCE
Volume 53, Issue 11, Pages 13452-13469Publisher
SPRINGER
DOI: 10.1007/s10489-022-04151-6
Keywords
Task allocation; Spatio-temporal crowdsourcing; Markov model; Q-learning; Policy gradient
Categories
Ask authors/readers for more resources
With the widespread use of dynamic task allocation in sharing economy applications, online bipartite graph matching has become a focus of research. This paper proposes a dynamic delay bipartite matching (DDBM) problem and designs two task allocation frameworks to increase allocation utility.
With the pervasiveness of dynamic task allocation in sharing economy applications, online bipartite graph matching has attracted more and more research attention. In sharing economy applications, crowdsourcing platforms need to allocate tasks to workers dynamically. Previous studies have low allocation utility. To increase the allocation utility of the Spatio-temporal crowdsourcing system, this paper proposes a dynamic delay bipartite matching(DDBM) problem, and designs Value Based Task Allocation(VBTA) and Policy Gradient Based Task Allocation(PGTA) frameworks respectively. According to the current state, VBTA and PGTA could enhance the allocation utility by selecting appropriate thresholds. The convergence of the algorithm is proved. Extensive experimental results on two real datasets demonstrate that the proposed algorithms are superior to the existing algorithms in effectiveness and efficiency.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available