{"type":"ScholarlyArticle","title":"Profit-driven Task Assignment in Spatial Crowdsourcing","authors":[{"name":"Jinfu Xia","slug":"jinfu-xia","given":"Jinfu","family":"Xia","affiliation":"Institute of Artificial Intelligence , School of Computer Science and Technology , Soochow University"},{"name":"Yan Zhao","slug":"yan-zhao","given":"Yan","family":"Zhao","affiliation":"Institute of Artificial Intelligence , School of Computer Science and Technology , Soochow University"},{"name":"Guanfeng Liu","slug":"guanfeng-liu","given":"Guanfeng","family":"Liu","affiliation":"Macquarie University"},{"name":"Jiajie Xu","slug":"jiajie-xu","given":"Jiajie","family":"Xu","affiliation":"Institute of Artificial Intelligence , School of Computer Science and Technology , Soochow University"},{"name":"Min Zhang","slug":"min-zhang","given":"Min","family":"Zhang","affiliation":"Institute of Artificial Intelligence , School of Computer Science and Technology , Soochow University"},{"name":"Kai Zheng","slug":"kai-zheng","given":"Kai","family":"Zheng","affiliation":"University of Electronic Science and Technology of China"}],"abstract":"In Spatial Crowdsourcing (SC) systems, mobile users are enabled to perform spatio-temporal tasks by physically traveling to specified locations with the SC platforms. SC platforms manage the systems and recruit mobile users to contribute to the SC systems, whose commercial success depends on the profit attained from the task requesters. In order to maximize its profit, an SC platform needs an online management mechanism to assign the tasks to suitable workers. How to assign the tasks to workers more cost-effectively with the spatio-temporal constraints is one of the most difficult problems in SC. To deal with this challenge, we propose a novel Profit-driven Task Assignment (PTA) problem, which aims to maximize the profit of the platform. Specifically, we first establish a task reward pricing model with tasks' temporal constraints (i.e., expected completion time and deadline). Then we adopt an optimal algorithm based on tree decomposition to achieve the optimal task assignment and propose greedy algorithms to improve the computational efficiency. Finally, we conduct extensive experiments using real and synthetic datasets, verifying the practicability of our proposed methods.","doi":"10.24963/ijcai.2019/265","url":"https://ijcai.org/proceedings/2019/265","pdf":"https://ijcai.org/proceedings/2019/0265.pdf","markdown":"https://ijcai.org/proceedings/2019/265.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/265","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Knowledge Representation and Reasoning","track":"Main track","number":265,"pages":{"first":1914,"last":1920},"keywords":["Humans and AI: Human Computation and Crowdsourcing","Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning"],"videos":[],"references":[{"position":1,"raw":"[Chen et al., 2014] Zhao Chen, Rui Fu, Ziyuan Zhao, Zheng Liu, Leihao Xia, Lei Chen, Peng Cheng, Caleb Chen Cao, Yongxin Tong, and Chen Jason Zhang. gmission: A gen- eral spatial crowdsourcing platform. 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