  There has been significant interest in crowdsourcing and human computation.
One subclass of human computation applications are those directed at tasks that
involve planning (e.g. travel planning) and scheduling (e.g. conference
scheduling). Much of this work appears outside the traditional automated
planning forums, and at the outset it is not clear whether automated planning
has much of a role to play in these human computation systems. Interestingly
however, work on these systems shows that even primitive forms of automated
oversight of the human planner does help in significantly improving the
effectiveness of the humans/crowd. In this paper, we will argue that the
automated oversight used in these systems can be viewed as a primitive
automated planner, and that there are several opportunities for more
sophisticated automated planning in effectively steering crowdsourced planning.
Straightforward adaptation of current planning technology is however hampered
by the mismatch between the capabilities of human workers and automated
planners. We identify two important challenges that need to be overcome before
such adaptation of planning technology can occur: (i) interpreting the inputs
of the human workers (and the requester) and (ii) steering or critiquing the
plans being produced by the human workers armed only with incomplete domain and
preference models. In this paper, we discuss approaches for handling these
challenges, and characterize existing human computation systems in terms of the
specific choices they make in handling these challenges.
