Crowd-Powered Source Searching in Complex Environments

Source searching algorithms are widely used in different domains and for various applications, for instance, to find gas or signal sources. As source searching algorithms advance, search problems need to be addressed in increasingly complex environments. Such environments could be high-dimensional and highly dynamic. Therefore, novel search algorithms have been designed, combining heuristic methods and intelligent optimization, to tackle search problems in large and complex search space. However, these intelligent search algorithms usually cannot guarantee completeness and optimality, and therefore commonly suffer from the problems such as local optimum. Recent studies have used crowd-powered systems to address the complex problems that machines cannot solve on their own. While leveraging human rationales in a computer system has been shown to be effective in making a system more reliable, whether using the power of the crowd can improve source searching algorithms remains unanswered. To this end, we propose a crowd-powered sourcing search approach, using human rationales as external supports to improve existing search algorithms, and meanwhile to minimize the human effort using machine predictions. Furthermore, we designed a prototype system, and carried out an experiment with 10 participants (4 experts and 6 non-experts). Quantitative and qualitative analysis showed that the sourcing search algorithm enhanced by crowd could achieve both high effectiveness and efficiency. Our work provides valuable insights in human-computer collaborative system design.