  Outsourcing tasks to previously unknown parties is becoming more common. One
specific such problem involves matching a set of workers to a set of tasks.
Even if the latter have precise requirements, the quality of individual workers
is usually unknown. The problem is thus a version of matching under
uncertainty. We believe that this type of problem is going to be increasingly
important.
  When the problem involves only a single skill or type of job, it is
essentially a type of bandit problem, and can be solved with standard
algorithms. However, we develop an algorithm that can perform matching for
workers with multiple skills hired for multiple jobs with multiple
requirements. We perform an experimental evaluation in both single-task and
multi-task problems, comparing with the bounded $\epsilon$-first algorithm, as
well as an oracle that knows the true skills of workers. One of the algorithms
we developed gives results approaching 85\% of oracle's performance. We invite
the community to take a closer look at this problem and develop real-world
benchmarks.
