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.

The Corona Virus Disease 2019 (COVID-19) pandemic is still imposing a devastating impact on public health, the economy, and society. Predicting the development of epidemics and exploring the effects of various mitigation strategies have been a research focus in recent years. However, the spread simulation of COVID-19 in the dynamic social system is relatively unexplored. To address this issue, considering the outbreak of COVID-19 at Nanjing Lukou Airport in 2021, we constructed an artificial society of Nanjing Lukou Airport based on the Artificial societies, Computational experiments, and Parallel execution (ACP) approach. Specifically, the artificial society includes an environmental model, population model, contact networks model, disease spread model, and intervention strategy model. To reveal the dynamic variation of individuals in the airport, we first modeled the movement of passengers and designed an algorithm to generate the moving traces. Then, the mobile contact networks were constructed and aggregated with the static networks of staff and passengers. Finally, the complex dynamical network of contacts between individuals was generated. Based on the artificial society, we conducted large-scale computational experiments to study the spread characteristics of COVID-19 in an airport and to investigate the effects of different intervention strategies. Learned from the reproduction of the outbreak, it is found that the increase in cumulative incidence exhibits a linear growth mode, different from that (an exponential growth mode) in a static network. In terms of mitigation measures, promoting unmanned security checks and boarding in an airport is recommended, as to reduce contact behaviors between individuals and staff.