Leveraging Human-AI Collaboration in Crowd-Powered Source Search: A Preliminary Study.

Source search is an important problem in our society, relating to finding fire sources, gas sources, or signal sources. Particularly, in an unexplored and potentially dangerous environment, an autonomous source search algorithm that employs robotic searchers is usually applied to address the problem. Such environments could be completely unknown and highly complex. Therefore, novel search algorithms have been designed, combining heuristic methods and intelligent optimization, to tackle search problems in large and complex search spaces. However, these intelligent search algorithms were not designed to address completeness and optimality, and therefore commonly suffer from the problems such as local optimums or endless loops. Recent studies have used crowd-powered systems to address the complex problems that cannot be solved by machines on their own. While leveraging human intelligence in an AI system has been shown to be effective in making the system more reliable, whether using the power of the crowd can improve autonomous source search algorithms remains unanswered. To this end, we propose a crowd-powered source search approach enabling human-AI collaboration, which uses human intelligence as external supports to improve existing search algorithms and meanwhile reduces human efforts using AI predictions. Furthermore, we designed a crowd-powered prototype system and carried out an experiment with both experts and non-experts, to complete 200 source search scenarios (704 crowdsourcing tasks). 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-AI collaborative system design.