A Multi-armed Bandit Approach to Online Spatial Task Assignment

Hassan, Umair ul; Curry, Edward

doi:10.1109/uic-atc-scalcom.2014.68

Cited by 53 publications

(44 citation statements)

References 18 publications

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“…In [36], micro tasks are allocated when both tasks and workers can appear anywhere, anytime. Different from our work, [32] learns the workers' acceptance probability in dynamic tasks static workers setup. Recall, we assume acceptance depends on the hardness level of the tasks and independent from previous acceptances.…”

Section: Crowdsourcingmentioning

confidence: 99%

Fair Task Allocation in Crowdsourced Delivery

Basık

Gedik

Ferhatosmanoğlu

et al. 2021

IEEE Trans. Serv. Comput.

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Faster and more cost-efficient, crowdsourced delivery is needed to meet the growing customer demands of many industries, including online shopping, on-demand local delivery, and on-demand transportation. The power of crowdsourced delivery stems from the large number of workers potentially available to provide services and reduce costs. It has been shown in social psychology literature that fairness is key to ensuring high worker participation. However, existing assignment solutions fall short on modeling the dynamic fairness metric. In this work, we introduce a new assignment strategy for crowdsourced delivery tasks. This strategy takes fairness towards workers into consideration, while maximizing the task allocation ratio. Since redundant assignments are not possible in delivery tasks, we first introduce a 2-phase allocation model that increases the reliability of a worker to complete a given task. To realize the effectiveness of our model in practice, we present both offline and online versions of our proposed algorithm called F-Aware. Given a task-to-worker bipartite graph, F-Aware assigns each task to a worker that minimizes unfairness, while allocating tasks to use worker capacities as much as possible. We present an evaluation of our algorithms with respect to running time, task allocation ratio (TAR), as well as unfairness and assignment ratio. Experiments show that F-Aware runs around 10 7 × faster than the TAR-optimal solution and allocates 96.9% of the tasks that can be allocated by it. Moreover, it is shown that, F-Aware is able to provide a much fair distribution of tasks to workers than the best competitor algorithm.

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Section: Crowdsourcingmentioning

confidence: 99%

Fair Task Allocation in Crowdsourced Delivery

Basık

Gedik

Ferhatosmanoğlu

et al. 2021

IEEE Trans. Serv. Comput.

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show abstract

“…However, in contrast to our work, [6] and [19] assume that worker context is centrally gathered, that workers always accept assigned tasks within certain known bounds and that worker skills are known a priori. In [20], an online task assignment algorithm is proposed for spatial CS with SAT mode for maximizing the expected number of accepted tasks. The problem is modeled as a contextual multi-armed bandit problem, and workers are selected for sequentially arriving tasks.…”

Section: Related Workmentioning

confidence: 99%

“…LinUCB has an input parameter λ LinUCB , controlling the influence of the confidence bound. Lin-UCB is used in [20] for task assignment in spatial CS. • AUER [36] is an extension of the well-known UCB algorithm [37] to the sleeping arm case.…”

Section: A Reference Algorithmsmentioning

confidence: 99%

Context-Aware Hierarchical Online Learning for Performance Maximization in Mobile Crowdsourcing

Müller

Tekin

Schaar

et al. 2018

IEEE/ACM Trans. Networking

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In mobile crowdsourcing (MCS), mobile users accomplish outsourced human intelligence tasks. MCS requires an appropriate task assignment strategy, since different workers may have different performance in terms of acceptance rate and quality. Task assignment is challenging, since a worker's performance (i) may fluctuate, depending on both the worker's current personal context and the task context, (ii) is not known a priori, but has to be learned over time. Moreover, learning context-specific worker performance requires access to context information, which may not be available at a central entity due to communication overhead or privacy concerns. Additionally, evaluating worker performance might require costly quality assessments. In this paper, we propose a context-aware hierarchical online learning algorithm addressing the problem of performance maximization in MCS. In our algorithm, a local controller (LC) in the mobile device of a worker regularly observes the worker's context, her/his decisions to accept or decline tasks and the quality in completing tasks. Based on these observations, the LC regularly estimates the worker's context-specific performance. The mobile crowdsourcing platform (MCSP) then selects workers based on performance estimates received from the LCs. This hierarchical approach enables the LCs to learn context-specific worker performance and it enables the MCSP to select suitable workers. In addition, our algorithm preserves worker context locally, and it keeps the number of required quality assessments low. We prove that our algorithm converges to the optimal task assignment strategy. Moreover, the algorithm outperforms simpler task assignment strategies in experiments based on synthetic and real data.

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“…We go through the following toy example to illustrate it. W2 (1,8) W3 (3,7) W4 (8,2) W5 (1,6) W6 (6,4) t1 (2,5) t2 (3,6) t6 (5,6) t4 (6,5) t3 (6,7) t5 (7,6) (a) Initial locations W2 (1,8) W3 (3,7) W4 (8,2) W5 (1,6) W6 (6,4) t1 (2,5) t2 (3,6) t6 (5,6) t4 (6,5) t3 (6,7) t5 (7,6) (b) OPT result W2 (1,8) W3 (3,7) W4 (8,2) W5 (1,6) W6 (6,...…”

Section: Introductionmentioning

confidence: 99%

Budget-aware online task assignment in spatial crowdsourcing

Liu

2019

World Wide Web

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The prevalence of mobile internet techniques stimulates the emergence of various spatial crowdsourcing applications. Certain of the applications serve for requesters, budget providers, who submit a batch of tasks and a fixed budget to platform with the desire to search suitable workers to complete the tasks in maximum quantity. Platform lays stress on optimizing assignment strategies on seeking less budget-consumed worker-task pairs to meet requesters' demands. Existing research on the task assignment with budget constraint mostly focuses on static offline scenarios, where the spatiotemporal information of all workers and tasks is known in advance. However, workers usually appear dynamically on real spatial crowdsourcing platforms, where existing solutions can hardly handle it. In this paper, we formally define a novel problem Budget-aware Online task Assignment(BOA) in spatial crowdsourcing applications. BOA aims to maximize the number of assigned workertask pairs under a budget constraint where workers appear dynamically on platforms. To address the BOA problem, we first propose an efficient threshold-based greedy algorithm Greedy-RT which utilizes a random generated threshold to prune the pairs with large travel cost. Greedy-RT performs well in adversary model when compared with simple greedy algorithm, but it is unstable in random model for its randomly generated threshold may produce poor quality in matching size. We then propose a revised algorithm Greedy-OT which could learn approximately optimal threshold from historical data, and consequently improves matching size significantly in both models. Finally, we verify the effectiveness and efficiency of the proposed methods through extensive experiments on real and synthetic datasets.

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A Multi-armed Bandit Approach to Online Spatial Task Assignment

Cited by 53 publications

References 18 publications

Fair Task Allocation in Crowdsourced Delivery

Fair Task Allocation in Crowdsourced Delivery

Context-Aware Hierarchical Online Learning for Performance Maximization in Mobile Crowdsourcing

Budget-aware online task assignment in spatial crowdsourcing

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