Task assignment in microtask crowdsourcing platforms using learning automata. (September 2018)

• Record Type: Journal Article
• Authors: Moayedikia, Alireza; Ong, Kok-Leong; Boo, Yee Ling; Yeoh, William G.S.
• Is Part Of: Engineering applications of artificial intelligence. Volume 74(2018)
• Page Start: 212
• Page End: 225
• Publication Date: 2018-09
• Subjects: Crowdsourcing -- Microtasking -- Learning automata -- Reinforcement learning -- Task assignment; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2018.06.008
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Conventional microtask crowdsourcing platforms rely on a random task distribution strategy and repeatedly assign tasks to workers. This strategy known as repeated labelling suffers from two shortcomings of high cost and low accuracy as a result of making random distributions. To overcome such shortcomings researchers have introduced task assignment as a substitute strategy. In this strategy, an algorithm selectively chooses suitable tasks for an online worker. Hence, task assignment has gained attentions from researchers to reduce the cost of microtasking whiling increasing its accuracy. However, the existing algorithms on task assignment suffer from four shortcomings as: (i) human intervention, (ii) reliance on a rough estimation of ground truth, (iii) reliance on workers' dynamic capabilities and (iv) lack of ability in dealing with sparsity. To overcome these shortcomings this paper proposes a new task assignment algorithm known as LEarning Automata based Task assignment (LEATask), that works based on the similarities of workers in performance. This algorithm has two stages of exploration and exploitation. In exploration stage, first a number of workers are hired to learn their reliability. Then, LEATask clusters the hired workers using a given clustering algorithm, and for each cluster generates learning automata. Later, the clusters of workers along with their attached learning automata will be used in exploitation stage. Exploitation stage initially assigns a number of tasks to a newly arrived worker to learn the worker's reliability. Then, LEATask identifies the cluster of worker. Based on the cluster that worker resides in and the attached learning automata, the next tasks will be assigned to the new worker. LEATask has been empirically evaluated using several real datasets and compared against the baseline and novel algorithms, in terms of root mean square error. The comparisons indicates LEATask consistently is showing better or comparable performance.