Abstract:
Online spatial crowdsourcing is inherently dynamic and uncertainty. Static methods might fail when addressing crowdsourcing problems of the online environments where the ...Show MoreMetadata
Abstract:
Online spatial crowdsourcing is inherently dynamic and uncertainty. Static methods might fail when addressing crowdsourcing problems of the online environments where the optimization parameters are unknown in advance; and crowd-workers can join or leave the environments in runtime. Thus, the goal of a spatial task assignment is to deal with dynamic and uncertainty features inherent in online crowdsourcing environments. Moreover, dynamic spatial task assignment is further complicated when there are large-scale tasks and crowd-workers in crowdsourcing environments. Therefore in this paper, we propose a dynamic optimization algorithm based on Spatial-aware Multi-Agent Q-Learning (SMAQL), which can keep higher effectiveness in face of uncertainty and large-scale problems in complex crowdsourcing scenario. The proposed approach combines spatial optimization with multi-agent Q-Iearning. S-MAQL algorithm decomposes the crowdsourcing optimization problem into many sub-problems by means of a grid-based spatial optimization approach. And each agent utilizes Q-Iearning algorithms to handle its own region optimization problems of the real-time crowdsourcing. Through extensive experiments, we demonstrate the efficiency of the proposed algorithms on the real data sets.
Published in: 2018 IEEE 22nd International Conference on Computer Supported Cooperative Work in Design ((CSCWD))
Date of Conference: 09-11 May 2018
Date Added to IEEE Xplore: 16 September 2018
ISBN Information: