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We investigate the problem of heterogeneous task assignment in crowdsourcingmarkets from the point of view of the requester, who has a collection of tasks.Workers arrive online one by one, and each declare a set of feasible tasks theycan solve, and desired payment for each feasible task. The requester mustdecide on the fly which task (if any) to assign to the worker, while assigningworkers only to feasible tasks. The goal is to maximize the number of assignedtasks with a fixed overall budget.  We provide an online algorithm for this problem and prove an upper bound onthe competitive ratio of this algorithm against an arbitrary (possiblyworst-case) sequence of workers who want small payments relative to therequester's total budget. We further show an almost matching lower bound on thecompetitive ratio of any algorithm in this setting. Finally, we propose adifferent algorithm that achieves an improved competitive ratio in the randompermutation model, where the order of arrival of the workers is chosenuniformly at random. Apart from these strong theoretical guarantees, we carryout experiments on simulated data which demonstrates the practicalapplicability of our algorithms.

Online Assignment of Heterogeneous Tasks in Crowdsourcing Markets