  Solving optimal design problems through crowdsourcing faces a dilemma: On one
hand, human beings have been shown to be more effective than algorithms at
searching for good solutions of certain real-world problems with
high-dimensional or discrete solution spaces; on the other hand, the cost of
setting up crowdsourcing environments, the uncertainty in the crowd's
domain-specific competence, and the lack of commitment of the crowd, all
contribute to the lack of real-world application of design crowdsourcing. We
are thus motivated to investigate a solution-searching mechanism where an
optimization algorithm is tuned based on human demonstrations on solution
searching, so that the search can be continued after human participants abandon
the problem. To do so, we model the iterative search process as a Bayesian
Optimization (BO) algorithm, and propose an inverse BO (IBO) algorithm to find
the maximum likelihood estimators of the BO parameters based on human
solutions. We show through a vehicle design and control problem that the search
performance of BO can be improved by recovering its parameters based on an
effective human search. Thus, IBO has the potential to improve the success rate
of design crowdsourcing activities, by requiring only good search strategies
instead of good solutions from the crowd.
