Learning an Optimization Algorithm through Human Design Iterations
This work addresses the inefficiency and cost issues in design crowdsourcing for engineering and optimization domains, offering an incremental improvement by automating search continuation.
The paper tackles the challenge of design crowdsourcing by proposing an inverse Bayesian Optimization (IBO) algorithm that learns optimization parameters from human search demonstrations, enabling continued search after human abandonment. In a vehicle design and control problem, IBO improved BO search performance, potentially boosting crowdsourcing success rates by leveraging human strategies rather than final solutions.
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.